Large language model

1126: 1646: 1117: 3423:. In their study, they examined and confirmed the possibility that questioners could get, from ChatGPT, the training data that the AI model used. For example, when asking ChatGPT 3.5 turbo to repeat the word "poem" forever, the AI model will say "poem" hundreds of times and then diverge, deviating from the standard dialogue style and spitting out nonsense phrases, thus spitting out the training data as it is. The researchers have seen more than 10,000 examples of the AI model exposing their training data in a similar method. The researchers said that it was hard to tell if the AI model was actually safe or not. 1149: 1656:-hours, while in 2020 the cost of training a 1.5-billion-parameter LLM (which was two orders of magnitude smaller than the state of the art in 2020) was between $ 80 thousand and $ 1.6 million. Since 2020, large sums were invested in increasingly large models. For example, training of the GPT-2 (i.e. a 1.5-billion-parameters model) in 2019 cost $ 50,000, while training of the PaLM (i.e. a 540-billion-parameters model) in 2022 cost $ 8 million, and Megatron-Turing NLG 530B (in 2021) cost around $ 11 million. 1672:

calculation in its training corpus. In such cases, the LLM needs to resort to running program code that calculates the result, which can then be included in its response. : Another example is 'What is the time now? It is ', where a separate program interpreter would need to execute a code to get system time on the computer, so LLM could include it in its reply. This basic strategy can be sophisticated with multiple attempts of generated programs, and other sampling strategies.

1563: 1584:, presented in February 2024, can have a context window sized up to 1 million (context window of 10 million was also "successfully tested"). Other models with large context windows includes Anthropic's Claude 2.1, with a context window of up to 200k tokens. Note that this maximum refers to the number of input tokens and that the maximum number of output tokens differs from the input and is often smaller. For example, the GPT-4 Turbo model has a maximum output of 4096 tokens. 2423: 3300:

expected answer can be derived (for example, the previous question could be adjoined with some text which includes the sentence "The Sharks have advanced to the Stanley Cup finals once, losing to the Pittsburgh Penguins in 2016."). Otherwise, the task is considered "closed book", and the model must draw on knowledge retained during training. Some examples of commonly used question answering datasets include TruthfulQA, Web Questions, TriviaQA, and SQuAD.

3311:, BIG-bench, and HELM. OpenAI has released tools for running composite benchmarks, but noted that the eval results are sensitive to the prompting method. Some public datasets contain questions that are mislabeled, ambiguous, unanswerable, or otherwise of low-quality, which can be cleaned to give more reliable benchmark scores. 3332:

with more challenging tasks. In addition, there are cases of "shortcut learning" wherein AIs sometimes "cheat" on multiple-choice tests by using statistical correlations in superficial test question wording in order to guess the correct responses, without necessarily understanding the actual question being asked.

2860:", and believes that RLHF tuning creates a "smiling facade" obscuring the inner workings of the LLM: "If you don't push it too far, the smiley face stays on. But then you give it prompt, and suddenly you see this massive underbelly of insanity, of weird thought processes and clearly non-human understanding." 2867:, or they point to the deficits existing LLMs continue to have in prediction skills, reasoning skills, agency, and explainability. For example, GPT-4 has natural deficits in planning and in real-time learning. Generative LLMs have been observed to confidently assert claims of fact which do not seem to be 10395:

Thoppilan, Romal; De Freitas, Daniel; Hall, Jamie; Shazeer, Noam; Kulshreshtha, Apoorv; Cheng, Heng-Tze; Jin, Alicia; Bos, Taylor; Baker, Leslie; Du, Yu; Li, YaGuang; Lee, Hongrae; Zheng, Huaixiu Steven; Ghafouri, Amin; Menegali, Marcelo (2022-01-01). "LaMDA: Language Models for Dialog Applications".

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Smith, Shaden; Patwary, Mostofa; Norick, Brandon; LeGresley, Patrick; Rajbhandari, Samyam; Casper, Jared; Liu, Zhun; Prabhumoye, Shrimai; Zerveas, George; Korthikanti, Vijay; Zhang, Elton; Child, Rewon; Aminabadi, Reza Yazdani; Bernauer, Julie; Song, Xia (2022-02-04). "Using DeepSpeed and Megatron to

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Brown, Tom B.; Mann, Benjamin; Ryder, Nick; Subbiah, Melanie; Kaplan, Jared; Dhariwal, Prafulla; Neelakantan, Arvind; Shyam, Pranav; Sastry, Girish; Askell, Amanda; Agarwal, Sandhini; Herbert-Voss, Ariel; Krueger, Gretchen; Henighan, Tom; Child, Rewon; Ramesh, Aditya; Ziegler, Daniel M.; Wu, Jeffrey;

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Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (2023). "Sparks of Artificial General Intelligence: Early experiments with GPT-4".

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Political bias refers to the tendency of algorithms to systematically favor certain political viewpoints, ideologies, or outcomes over others. Language models may also exhibit political biases. Since the training data includes a wide range of political opinions and coverage, the models might generate

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Typically, LLMs are trained with single- or half-precision floating point numbers (float32 and float16). One float16 has 16 bits, or 2 bytes, and so one billion parameters require 2 gigabytes. The largest models typically have 100 billion parameters, requiring 200 gigabytes to load, which places them

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The potential presence of "sleeper agents" within LLM models is another emerging security concern. These are hidden functionalities built into the model that remain dormant until triggered by a specific event or condition. Upon activation, the LLM deviates from its expected behavior to make insecure

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has the same dimensions as an encoded token. That is an "image token". Then, one can interleave text tokens and image tokens. The compound model is then fine-tuned on an image-text dataset. This basic construction can be applied with more sophistication to improve the model. The image encoder may be

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When each head calculates, according to its own criteria, how much other tokens are relevant for the "it_" token, note that the second attention head, represented by the second column, is focusing most on the first two rows, i.e. the tokens "The" and "animal", while the third column is focusing most

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In the context of training LLMs, datasets are typically cleaned by removing toxic passages from the dataset, discarding low-quality data, and de-duplication. Cleaned datasets can increase training efficiency and lead to improved downstream performance. A trained LLM can be used to clean datasets for

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The training compute of notable large models in FLOPs vs publication date over the period 2010-2024. For overall notable models (top left), frontier models (top right), top language models (bottom left) and top models within leading companies (bottom right). The majority of these models are language

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One broad category of evaluation dataset is question answering datasets, consisting of pairs of questions and correct answers, for example, ("Have the San Jose Sharks won the Stanley Cup?", "No"). A question answering task is considered "open book" if the model's prompt includes text from which the

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on a given text corpus. Perplexity is a measure of how well a model is able to predict the contents of a dataset; the higher the likelihood the model assigns to the dataset, the lower the perplexity. Mathematically, perplexity is defined as the exponential of the average negative log likelihood per

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The Reflexion method constructs an agent that learns over multiple episodes. At the end of each episode, the LLM is given the record of the episode, and prompted to think up "lessons learned", which would help it perform better at a subsequent episode. These "lessons learned" are given to the agent

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With the increasing proportion of LLM-generated content on the web, data cleaning in the future may include filtering out such content. LLM-generated content can pose a problem if the content is similar to human text (making filtering difficult) but of lower quality (degrading performance of models

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A token vocabulary based on the frequencies extracted from mainly English corpora uses as few tokens as possible for an average English word. An average word in another language encoded by such an English-optimized tokenizer is however split into suboptimal amount of tokens. GPT-2 tokenizer can use

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Zhang, Susan; Roller, Stephen; Goyal, Naman; Artetxe, Mikel; Chen, Moya; Chen, Shuohui; Dewan, Christopher; Diab, Mona; Li, Xian; Lin, Xi Victoria; Mihaylov, Todor; Ott, Myle; Shleifer, Sam; Shuster, Kurt; Simig, Daniel; Koura, Punit Singh; Sridhar, Anjali; Wang, Tianlu; Zettlemoyer, Luke (21 June

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Wang, Shuohuan; Sun, Yu; Xiang, Yang; Wu, Zhihua; Ding, Siyu; Gong, Weibao; Feng, Shikun; Shang, Junyuan; Zhao, Yanbin; Pang, Chao; Liu, Jiaxiang; Chen, Xuyi; Lu, Yuxiang; Liu, Weixin; Wang, Xi; Bai, Yangfan; Chen, Qiuliang; Zhao, Li; Li, Shiyong; Sun, Peng; Yu, Dianhai; Ma, Yanjun; Tian, Hao; Wu,

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Ouyang, Long; Wu, Jeff; Jiang, Xu; Almeida, Diogo; Wainwright, Carroll L.; Mishkin, Pamela; Zhang, Chong; Agarwal, Sandhini; Slama, Katarina; Ray, Alex; Schulman, John; Hilton, Jacob; Kelton, Fraser; Miller, Luke; Simens, Maddie; Askell, Amanda; Welinder, Peter; Christiano, Paul; Leike, Jan; Lowe,

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Notably, gender bias refers to the tendency of these models to produce outputs that are unfairly prejudiced towards one gender over another. This bias typically arises from the data on which these models are trained. Large language models often assign roles and characteristics based on traditional

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Notably, in the case of larger language models that predominantly employ sub-word tokenization, bits per token (BPT) emerges as a seemingly more appropriate measure. However, due to the variance in tokenization methods across different Large Language Models (LLMs), BPT does not serve as a reliable

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outlines how specific neural structures of the human brain shape the nature of thought and language and in turn what are the computational properties of such neural systems that can be applied to model thought and language in a computer system. After a framework for modeling language in a computer

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out of an LLM, using the LLM as a planner. The LLM is prompted to "think out loud". Specifically, the language model is prompted with a textual description of the environment, a goal, a list of possible actions, and a record of the actions and observations so far. It generates one or more thoughts

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Köpf, Andreas; Kilcher, Yannic; von Rütte, Dimitri; Anagnostidis, Sotiris; Tam, Zhi-Rui; Stevens, Keith; Barhoum, Abdullah; Duc, Nguyen Minh; Stanley, Oliver; Nagyfi, Richárd; ES, Shahul; Suri, Sameer; Glushkov, David; Dantuluri, Arnav; Maguire, Andrew (2023-04-14). "OpenAssistant Conversations –

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Driess, Danny; Xia, Fei; Sajjadi, Mehdi S. M.; Lynch, Corey; Chowdhery, Aakanksha; Ichter, Brian; Wahid, Ayzaan; Tompson, Jonathan; Vuong, Quan; Yu, Tianhe; Huang, Wenlong; Chebotar, Yevgen; Sermanet, Pierre; Duckworth, Daniel; Levine, Sergey (2023-03-01). "PaLM-E: An Embodied Multimodal Language

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Because of the rapid pace of improvement of large language models, evaluation benchmarks have suffered from short lifespans, with state of the art models quickly "saturating" existing benchmarks, exceeding the performance of human annotators, leading to efforts to replace or augment the benchmark

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It was previously standard to report results on a heldout portion of an evaluation dataset after doing supervised fine-tuning on the remainder. It is now more common to evaluate a pre-trained model directly through prompting techniques, though researchers vary in the details of how they formulate

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of unseen data. This presents particular challenges for the evaluation of large language models. As they are trained on increasingly large corpora of text largely scraped from the web, it becomes increasingly likely that models' training data inadvertently includes portions of any given test set.

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pioneered statistical language modelling. A smoothed n-gram model in 2001 trained on 0.3 billion words achieved then-SOTA (state of the art) perplexity. In the 2000s, as Internet use became prevalent, some researchers constructed Internet-scale language datasets ("web as corpus"), upon which they

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Wayne Xin Zhao; Zhou, Kun; Li, Junyi; Tang, Tianyi; Wang, Xiaolei; Hou, Yupeng; Min, Yingqian; Zhang, Beichen; Zhang, Junjie; Dong, Zican; Du, Yifan; Yang, Chen; Chen, Yushuo; Chen, Zhipeng; Jiang, Jinhao; Ren, Ruiyang; Li, Yifan; Tang, Xinyu; Liu, Zikang; Liu, Peiyu; Nie, Jian-Yun; Wen, Ji-Rong

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wrote that "it is no longer possible to accurately distinguish" human-written text from text created by large language models, and that "It is all but certain that general-purpose large language models will rapidly proliferate... It is a rather safe bet that they will change many industries over

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Some datasets have been constructed adversarially, focusing on particular problems on which extant language models seem to have unusually poor performance compared to humans. One example is the TruthfulQA dataset, a question answering dataset consisting of 817 questions which language models are

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Large language model (LLM) applications accessible to the public, like ChatGPT or Claude, typically incorporate safety measures designed to filter out harmful content. However, implementing these controls effectively has proven challenging. For instance, research by Kang et al. demonstrated a

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Another example of an adversarial evaluation dataset is Swag and its successor, HellaSwag, collections of problems in which one of multiple options must be selected to complete a text passage. The incorrect completions were generated by sampling from a language model and filtering with a set of

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Lewkowycz, Aitor; Andreassen, Anders; Dohan, David; Dyer, Ethan; Michalewski, Henryk; Ramasesh, Vinay; Slone, Ambrose; Anil, Cem; Schlag, Imanol; Gutman-Solo, Theo; Wu, Yuhuai; Neyshabur, Behnam; Gur-Ari, Guy; Misra, Vedant (30 June 2022). "Solving Quantitative Reasoning Problems with Language

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In order to find out which tokens are relevant to each other within the scope of the context window, the attention mechanism calculates "soft" weights for each token, more precisely for its embedding, by using multiple attention heads, each with its own "relevance" for calculating its own soft

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While LLMs have shown remarkable capabilities in generating human-like text, they are susceptible to inheriting and amplifying biases present in their training data. This can manifest in skewed representations or unfair treatment of different demographics, such as those based on race, gender,

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Some commenters expressed concern over accidental or deliberate creation of misinformation, or other forms of misuse. For example, the availability of large language models could reduce the skill-level required to commit bioterrorism; biosecurity researcher Kevin Esvelt has suggested that LLM

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Ren, Xiaozhe; Zhou, Pingyi; Meng, Xinfan; Huang, Xinjing; Wang, Yadao; Wang, Weichao; Li, Pengfei; Zhang, Xiaoda; Podolskiy, Alexander; Arshinov, Grigory; Bout, Andrey; Piontkovskaya, Irina; Wei, Jiansheng; Jiang, Xin; Su, Teng; Liu, Qun; Yao, Jun (March 19, 2023). "PanGu-Σ: Towards Trillion

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Memorization is an emergent behavior in LLMs in which long strings of text are occasionally output verbatim from training data, contrary to typical behavior of traditional artificial neural nets. Evaluations of controlled LLM output measure the amount memorized from training data (focused on

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There are certain tasks that, in principle, cannot be solved by any LLM, at least not without the use of external tools or additional software. An example of such a task is responding to the user's input '354 * 139 = ', provided that the LLM has not already encountered a continuation of this

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Hoffmann, Jordan; Borgeaud, Sebastian; Mensch, Arthur; Buchatskaya, Elena; Cai, Trevor; Rutherford, Eliza; Casas, Diego de Las; Hendricks, Lisa Anne; Welbl, Johannes; Clark, Aidan; Hennigan, Tom; Noland, Eric; Millican, Katie; Driessche, George van den; Damoc, Bogdan (2022-03-29). "Training

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Winter, Clemens; Hesse, Christopher; Chen, Mark; Sigler, Eric; Litwin, Mateusz; Gray, Scott; Chess, Benjamin; Clark, Jack; Berner, Christopher; McCandlish, Sam; Radford, Alec; Sutskever, Ilya; Amodei, Dario (Dec 2020). Larochelle, H.; Ranzato, M.; Hadsell, R.; Balcan, M.F.; Lin, H. (eds.).

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Penedo, Guilherme; Malartic, Quentin; Hesslow, Daniel; Cojocaru, Ruxandra; Cappelli, Alessandro; Alobeidli, Hamza; Pannier, Baptiste; Almazrouei, Ebtesam; Launay, Julien (2023-06-01). "The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only".

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As stated in Technical report: "Given both the competitive landscape and the safety implications of large-scale models like GPT-4, this report contains no further details about the architecture (including model size), hardware, training compute, dataset construction, training method ..."

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The shortcomings of making a context window larger include higher computational cost and possibly diluting the focus on local context, while making it smaller can cause a model to miss an important long-range dependency. Balancing them are a matter of experimentation and domain-specific

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aims to decrease the space requirement by lowering precision of the parameters of a trained model, while preserving most of its performance. The simplest form of quantization simply truncates all numbers to a given number of bits. It can be improved by using a different quantization

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Models may be trained on auxiliary tasks which test their understanding of the data distribution, such as Next Sentence Prediction (NSP), in which pairs of sentences are presented and the model must predict whether they appear consecutively in the training corpus. During training,

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The matter of LLM's exhibiting intelligence or understanding has two main aspects – the first is how to model thought and language in a computer system, and the second is how to enable the computer system to generate human like language. These aspects of language as a model of

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NLP researchers were evenly split when asked, in a 2022 survey, whether (untuned) LLMs "could (ever) understand natural language in some nontrivial sense". Proponents of "LLM understanding" believe that some LLM abilities, such as mathematical reasoning, imply an ability to

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In the DEPS ("Describe, Explain, Plan and Select") method, an LLM is first connected to the visual world via image descriptions, then it is prompted to produce plans for complex tasks and behaviors based on its pretrained knowledge and environmental feedback it receives.

2823:. Similar to the Othello-GPT example, there is a linear representation of Karel program semantics, and modifying the representation changes output in the correct way. The model also generates correct programs that are on average shorter than those in the training set. 7152:

Liang, Yaobo; Wu, Chenfei; Song, Ting; Wu, Wenshan; Xia, Yan; Liu, Yu; Ou, Yang; Lu, Shuai; Ji, Lei; Mao, Shaoguang; Wang, Yun; Shou, Linjun; Gong, Ming; Duan, Nan (2023-03-01). "TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs".

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Gao, Leo; Biderman, Stella; Black, Sid; Golding, Laurence; Hoppe, Travis; Foster, Charles; Phang, Jason; He, Horace; Thite, Anish; Nabeshima, Noa; Presser, Shawn; Leahy, Connor (31 December 2020). "The Pile: An 800GB Dataset of Diverse Text for Language Modeling".

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certain concepts. A Microsoft team argued in 2023 that GPT-4 "can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more" and that GPT-4 "could reasonably be viewed as an early (yet still incomplete) version of an

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Winter, Clemens; Hesse, Christopher; Chen, Mark; Sigler, Eric; Litwin, Mateusz; Gray, Scott; Chess, Benjamin; Clark, Jack; Berner, Christopher; McCandlish, Sam; Radford, Alec; Sutskever, Ilya; Amodei, Dario (May 28, 2020). "Language Models are Few-Shot Learners".

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AI models can reinforce a wide range of stereotypes, including those based on gender, ethnicity, age, nationality, religion, or occupation. This can lead to outputs that unfairly generalize or caricature groups of people, sometimes in harmful or derogatory ways.

10423:. Proceedings of BigScience Episode #5 – Workshop on Challenges & Perspectives in Creating Large Language Models. Vol. Proceedings of BigScience Episode #5 – Workshop on Challenges & Perspectives in Creating Large Language Models. pp. 95–136. 6603:

Wei, Jason; Tay, Yi; Bommasani, Rishi; Raffel, Colin; Zoph, Barret; Borgeaud, Sebastian; Yogatama, Dani; Bosma, Maarten; Zhou, Denny; Metzler, Donald; Chi, Ed H.; Hashimoto, Tatsunori; Vinyals, Oriol; Liang, Percy; Dean, Jeff; Fedus, William (31 August 2022).

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Dettmers, Tim; Svirschevski, Ruslan; Egiazarian, Vage; Kuznedelev, Denis; Frantar, Elias; Ashkboos, Saleh; Borzunov, Alexander; Hoefler, Torsten; Alistarh, Dan (2023-06-01). "SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression".

2879:". Specifically, hallucinations in the context of LLMs correspond to the generation of text or responses that seem syntactically sound, fluent, and natural but are factually incorrect, nonsensical, or unfaithful to the provided source input. Neuroscientist 1310:("unknown") for characters not appearing in the vocabulary. Also, some special symbols are used to denote special text formatting. For example, "Ġ" denotes a preceding whitespace in RoBERTa and GPT. "##" denotes continuation of a preceding word in BERT. 3336:

susceptible to answering incorrectly by mimicking falsehoods to which they were repeatedly exposed during training. For example, an LLM may answer "No" to the question "Can you teach an old dog new tricks?" because of its exposure to the English idiom

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Alayrac, Jean-Baptiste; Donahue, Jeff; Luc, Pauline; Miech, Antoine; Barr, Iain; Hasson, Yana; Lenc, Karel; Mensch, Arthur; Millican, Katherine; Reynolds, Malcolm; Ring, Roman; Rutherford, Eliza; Cabi, Serkan; Han, Tengda; Gong, Zhitao (2022-12-06).

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model has had twelve attention heads and a context window of only 1k tokens. In its medium version it has 345M parameters and contains 24 layers, each with 12 attention heads. For the training with gradient descent a batch size of 512 was utilized.

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This means 1.5 Pro can process vast amounts of information in one go — including 1 hour of video, 11 hours of audio, codebases with over 30,000 lines of code or over 700,000 words. In our research, we've also successfully tested up to 10 million

1245:. As of June 2024, The Instruction fine tuned variant of the Llama 3 70 billion parameter model is the most powerful open LLM according to the LMSYS Chatbot Arena Leaderboard, being more powerful than GPT-3.5 but not as powerful as GPT-4. 11089: 6452:

Abdin, Marah; Jacobs, Sam Ade; Awan, Ammar Ahmad; Aneja, Jyoti; Awadallah, Ahmed; Awadalla, Hany; Bach, Nguyen; Bahree, Amit; Bakhtiari, Arash (2024-04-23). "Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone".

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Dey, Nolan; Gosal, Gurpreet; Zhiming; Chen; Khachane, Hemant; Marshall, William; Pathria, Ribhu; Tom, Marvin; Hestness, Joel (2023-04-01). "Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster".

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is generally the preferred metric over entropy. The underlying principle is that a lower BPW is indicative of a model's enhanced capability for compression. This, in turn, reflects the model's proficiency in making accurate predictions.

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A common method to create multimodal models out of an LLM is to "tokenize" the output of a trained encoder. Concretely, one can construct an LLM that can understand images as follows: take a trained LLM, and take a trained image encoder

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Lepikhin, Dmitry; Lee, HyoukJoong; Xu, Yuanzhong; Chen, Dehao; Firat, Orhan; Huang, Yanping; Krikun, Maxim; Shazeer, Noam; Chen, Zhifeng (2021-01-12). "GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding".

2439:" in the scaling law, where the slope of the line changes abruptly, and where larger models acquire "emergent abilities". They arise from the complex interaction of the model's components and are not explicitly programmed or designed. 1283:

algorithms process numbers rather than text, the text must be converted to numbers. In the first step, a vocabulary is decided upon, then integer indices are arbitrarily but uniquely assigned to each vocabulary entry, and finally, an

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Biderman, Stella; Schoelkopf, Hailey; Anthony, Quentin; Bradley, Herbie; Khan, Mohammad Aflah; Purohit, Shivanshu; Prashanth, USVSN Sai (April 2023). "Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling".

1591:, is longer than its context window, only the parts inside the context window are taken into account when generating the next answer, or the model needs to apply some algorithm to summarize the too distant parts of conversation. 6334:

Dodge, Jesse; Sap, Maarten; Marasović, Ana; Agnew, William; Ilharco, Gabriel; Groeneveld, Dirk; Mitchell, Margaret; Gardner, Matt (2021). "Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus".

1691:. Given a query, a document retriever is called to retrieve the most relevant documents. This is usually done by encoding the query and the documents into vectors, then finding the documents with vectors (usually stored in a 2818:

moves. It is found that there is a linear representation of Othello board, and modifying the representation changes the predicted legal Othello moves in the correct way. In another example, a small Transformer is trained on

2753: 1404:(i.e. initial set of uni-grams). Successively the most frequent pair of adjacent characters is merged into a bi-gram and all instances of the pair are replaced by it. All occurrences of adjacent pairs of (previously merged) 3303:

Evaluation datasets may also take the form of text completion, having the model select the most likely word or sentence to complete a prompt, for example: "Alice was friends with Bob. Alice went to visit her friend, ____".

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method for circumventing LLM safety systems. Similarly, Wang illustrated how a potential criminal could potentially bypass ChatGPT 4o's safety controls to obtain information on establishing a drug trafficking operation.

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Taylor, Ross; Kardas, Marcin; Cucurull, Guillem; Scialom, Thomas; Hartshorn, Anthony; Saravia, Elvis; Poulton, Andrew; Kerkez, Viktor; Stojnic, Robert (16 November 2022). "Galactica: A Large Language Model for Science".

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Training of largest language models might need more linguistic data than naturally available, or that the naturally occurring data is of insufficient quality. In these cases, synthetic data might be used. Microsoft's

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Generally, in order to get an LLM to use tools, one must finetune it for tool-use. If the number of tools is finite, then finetuning may be done just once. If the number of tools can grow arbitrarily, as with online

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Wu, Shijie; Irsoy, Ozan; Lu, Steven; Dabravolski, Vadim; Dredze, Mark; Gehrmann, Sebastian; Kambadur, Prabhanjan; Rosenberg, David; Mann, Gideon (March 30, 2023). "BloombergGPT: A Large Language Model for Finance".

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For open-ended exploration, an LLM can be used to score observations for their "interestingness", which can be used as a reward signal to guide a normal (non-LLM) reinforcement learning agent. Alternatively, it can

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Flamingo demonstrated the effectiveness of the tokenization method, finetuning a pair of pretrained language model and image encoder to perform better on visual question answering than models trained from scratch.

2487:. The authors considered a toy statistical model of an LLM solving multiple-choice questions, and showed that this statistical model, modified to account for other types of tasks, applies to these tasks as well. 2670: 2409: 1719:

before generating an action, which is then executed in the environment. The linguistic description of the environment given to the LLM planner can even be the LaTeX code of a paper describing the environment.

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Paranjape, Bhargavi; Lundberg, Scott; Singh, Sameer; Hajishirzi, Hannaneh; Zettlemoyer, Luke; Tulio Ribeiro, Marco (2023-03-01). "ART: Automatic multi-step reasoning and tool-use for large language models".

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Entropy, in this context, is commonly quantified in terms of bits per word (BPW) or bits per character (BPC), which hinges on whether the language model utilizes word-based or character-based tokenization.

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Li, Kenneth; Hopkins, Aspen K.; Bau, David; Viégas, Fernanda; Pfister, Hanspeter; Wattenberg, Martin (2022-10-01). "Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task".

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Lewis, Patrick; Perez, Ethan; Piktus, Aleksandra; Petroni, Fabio; Karpukhin, Vladimir; Goyal, Naman; Küttler, Heinrich; Lewis, Mike; Yih, Wen-tau; Rocktäschel, Tim; Riedel, Sebastian; Kiela, Douwe (2020).

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Shazeer, Noam; Mirhoseini, Azalia; Maziarz, Krzysztof; Davis, Andy; Le, Quoc; Hinton, Geoffrey; Dean, Jeff (2017-01-01). "Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer".

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Wang, Zihao; Cai, Shaofei; Liu, Anji; Ma, Xiaojian; Liang, Yitao (2023-02-03). "Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents".

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Performance of bigger models on various tasks, when plotted on a log-log scale, appears as a linear extrapolation of performance achieved by smaller models. However, this linearity may be punctuated by

1519:," an initial naive completion might be "If you submit the essay after March 17, your grade will be reduced by 10% for each day of delay," based on the frequency of this textual sequence in the corpus. 11086: 8626:

Varshney, Neeraj; Yao, Wenlin; Zhang, Hongming; Chen, Jianshu; Yu, Dong (2023). "A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation".

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LLM-powered agents can keep a long-term memory of its previous contexts, and the memory can be retrieved in the same way as Retrieval Augmented Generation. Multiple such agents can interact socially.

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Kaplan, Jared; McCandlish, Sam; Henighan, Tom; Brown, Tom B.; Chess, Benjamin; Child, Rewon; Gray, Scott; Radford, Alec; Wu, Jeffrey; Amodei, Dario (2020). "Scaling Laws for Neural Language Models".

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Wang, Yizhong; Kordi, Yeganeh; Mishra, Swaroop; Liu, Alisa; Smith, Noah A.; Khashabi, Daniel; Hajishirzi, Hannaneh (2022). "Self-Instruct: Aligning Language Model with Self Generated Instructions".

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have also been developed to evaluate the capabilities of language models on more specific downstream tasks. Tests may be designed to evaluate a variety of capabilities, including general knowledge,

1587:

Length of a conversation that the model can take into account when generating its next answer is limited by the size of a context window, as well. If the length of a conversation, for example with

1129:

The training compute of notable large AI models in FLOPs vs publication date over the period 2017-2024. The majority of large models are language models or multimodal models with language capacity.

10182:

Hua; Wu, Tian; Zeng, Wei; Li, Ge; Gao, Wen; Wang, Haifeng (December 23, 2021). "ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation".

2069: 7762: 1138:

trained statistical language models. In 2009, in most language processing tasks, statistical language models dominated over symbolic language models, as they can usefully ingest large datasets.

2327:, meaning that it costs 6 FLOPs per parameter to train on one token. Note that training cost is much higher than inference cost, where it costs 1 to 2 FLOPs per parameter to infer on one token. 1515:

correct responses, replacing any naive responses, starting from human-generated corrections of a few cases. For example, in the instruction "Write an essay about the main themes represented in

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Park, Joon Sung; O'Brien, Joseph C.; Cai, Carrie J.; Ringel Morris, Meredith; Liang, Percy; Bernstein, Michael S. (2023-04-01). "Generative Agents: Interactive Simulacra of Human Behavior".

9754:

Yang, Zhilin; Dai, Zihang; Yang, Yiming; Carbonell, Jaime; Salakhutdinov, Ruslan; Le, Quoc V. (2 January 2020). "XLNet: Generalized Autoregressive Pretraining for Language Understanding".

8754:

Clark, Christopher; Lee, Kenton; Chang, Ming-Wei; Kwiatkowski, Tom; Collins, Michael; Toutanova, Kristina (2019). "BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions".

2464:: Model outputs are improved by chain-of-thought prompting only when model size exceeds 62B. Smaller models perform better when prompted to answer immediately, without chain of thought. 11246: 8442: 10821: 8476: 9026: 10286: 5887: 10989: 10459: 9308:

Luo, Queenie; Puett, Michael J.; Smith, Michael D. (2023-03-28). "A Perspectival Mirror of the Elephant: Investigating Language Bias on Google, ChatGPT, Knowledge, and YouTube".

9287: 11692: 7696: 6698: 11662: 10067: 9507: 8182: 8053:

Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

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Shinn, Noah; Cassano, Federico; Labash, Beck; Gopinath, Ashwin; Narasimhan, Karthik; Yao, Shunyu (2023-03-01). "Reflexion: Language Agents with Verbal Reinforcement Learning".

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can use an LLM as rollout heuristic. When a programmatic world model is not available, an LLM can also be prompted with a description of the environment to act as world model.

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Li, Junnan; Li, Dongxu; Savarese, Silvio; Hoi, Steven (2023-01-01). "BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models".

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classifiers. The resulting problems are trivial for humans but at the time the datasets were created state of the art language models had poor accuracy on them. For example:

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In contrast, some proponents of the "LLMs lack understanding" school believe that existing LLMs are "simply remixing and recombining existing writing", a phenomenon known as

1416:, the size is 50257). After a tokenizer is trained, any text can be tokenized by it, as long as it does not contain characters not appearing in the initial-set of uni-grams. 1379:, the shorter texts must be "padded" until they match the length of the longest one. How many tokens are, on average, needed per word depends on the language of the dataset. 11858: 10520: 5948: 11798: 10770: 9554:

Patel, Ajay; Li, Bryan; Rasooli, Mohammad Sadegh; Constant, Noah; Raffel, Colin; Callison-Burch, Chris (2022). "Bidirectional Language Models Are Also Few-shot Learners".

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Lin, Zhenghao; Gou, Zhibin; Gong, Yeyun; Liu, Xiao; Shen, Yelong; Xu, Ruochen; Lin, Chen; Yang, Yujiu; Jiao, Jian (2024-04-11). "Rho-1: Not All Tokens Are What You Need".

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Yao, Shunyu; Zhao, Jeffrey; Yu, Dian; Du, Nan; Shafran, Izhak; Narasimhan, Karthik; Cao, Yuan (2022-10-01). "ReAct: Synergizing Reasoning and Acting in Language Models".

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LLM by discovering symbolic algorithms that approximate the inference performed by LLM. One example is Othello-GPT, where a small Transformer is trained to predict legal

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Soltan, Saleh; Ananthakrishnan, Shankar; FitzGerald, Jack; et al. (3 August 2022). "AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model".

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Hao, Shibo; Gu, Yi; Ma, Haodi; Jiahua Hong, Joshua; Wang, Zhen; Zhe Wang, Daisy; Hu, Zhiting (2023-05-01). "Reasoning with Language Model is Planning with World Model".

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Maslej, Nestor; Fattorini, Loredana; Brynjolfsson, Erik; Etchemendy, John; Ligett, Katrina; Lyons, Terah; Manyika, James; Ngo, Helen; Niebles, Juan Carlos (2023-10-05),

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Devlin, Jacob; Chang, Ming-Wei; Lee, Kenton; Toutanova, Kristina (11 October 2018). "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding".

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Devlin, Jacob; Chang, Ming-Wei; Lee, Kenton; Toutanova, Kristina (11 October 2018). "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding".

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intelligent?" Some researchers characterize LLMs as "alien intelligence". For example, Conjecture CEO Connor Leahy considers untuned LLMs to be like inscrutable alien "

1652:

Advances in software and hardware have reduced the cost substantially since 2020, such that in 2023 training of a 12-billion-parameter LLM computational cost is 72,300

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A model may be pre-trained either to predict how the segment continues, or what is missing in the segment, given a segment from its training dataset. It can be either

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As of 2024, the largest and most capable models are all based on the Transformer architecture. Some recent implementations are based on other architectures, such as

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Frantar, Elias; Ashkboos, Saleh; Hoefler, Torsten; Alistarh, Dan (2022-10-01). "GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers".

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After neural networks became dominant in image processing around 2012, they were applied to language modelling as well. Google converted its translation service to

922: 11211: 8558:

Ji, Ziwei; Lee, Nayeon; Frieske, Rita; Yu, Tiezheng; Su, Dan; Xu, Yan; Ishii, Etsuko; Bang, Yejin; Dai, Wenliang; Madotto, Andrea; Fung, Pascale (November 2022).

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In many cases, researchers release or report on multiple versions of a model having different sizes. In these cases, the size of the largest model is listed here.

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systems was established, the focus shifted to establishing frameworks for computer systems to generate language with acceptable grammar. In his 2014 book titled

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An illustration of main components of the transformer model from the original paper, where layers were normalized after (instead of before) multiheaded attention

17: 11956: 9186: 7080:

Gao, Luyu; Madaan, Aman; Zhou, Shuyan; Alon, Uri; Liu, Pengfei; Yang, Yiming; Callan, Jamie; Neubig, Graham (2022-11-01). "PAL: Program-aided Language Models".

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suggested in 2023 that generative language AI could increase global GDP by 7% in the next ten years, and could expose to automation 300 million jobs globally.

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language, and cultural groups. Since English data is overrepresented in current large language models' training data, it may also downplay non-English views.

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Nanda, Neel; Chan, Lawrence; Lieberum, Tom; Smith, Jess; Steinhardt, Jacob (2023-01-01). "Progress measures for grokking via mechanistic interpretability".

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For solving "mathematical and scientific questions using step-by-step reasoning". Based on PaLM model, further trained on mathematical and scientific data.

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Some composite benchmarks have also been developed which combine a diversity of different evaluation datasets and tasks. Examples include GLUE, SuperGLUE,

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has argued that "The diverging opinions of experts on the intelligence of LLMs suggests that our old ideas based on natural intelligence are inadequate".

10374: 7569: 869: 10959: 1269: 7740: 5538: 2716: 1695:) most similar to the vector of the query. The LLM then generates an output based on both the query and context included from the retrieved documents. 11276: 8988: 5375:

Facebook's license and distribution scheme restricted access to approved researchers, but the model weights were leaked and became widely available.

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released by EleutherAI. GPT-Neo outperformed an equivalent-size GPT-3 model on some benchmarks, but was significantly worse than the largest GPT-3.

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was introduced and quickly became "ubiquitous". Though the original transformer has both encoder and decoder blocks, BERT is an encoder-only model.

11560: 10345: 8152: 710: 9646: 6288:

Petrov, Aleksandar; Emanuele La Malfa; Torr, Philip H. S.; Bibi, Adel (2023). "Language Model Tokenizers Introduce Unfairness Between Languages".

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Yin, Shukang; Fu, Chaoyou; Zhao, Sirui; Li, Ke; Sun, Xing; Xu, Tong; Chen, Enhong (2023-06-01). "A Survey on Multimodal Large Language Models".

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Zhang, Jenny; Lehman, Joel; Stanley, Kenneth; Clune, Jeff (2 June 2023). "OMNI: Open-endedness via Models of human Notions of Interestingness".

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This is the license of the pre-trained model weights. In almost all cases the training code itself is open-source or can be easily replicated.

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Patil, Shishir G.; Zhang, Tianjun; Wang, Xin; Gonzalez, Joseph E. (2023-05-01). "Gorilla: Large Language Model Connected with Massive APIs".

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metric for comparative analysis among diverse models. To convert BPT into BPW, one can multiply it by the average number of tokens per word.

10813: 3338: 6310: 6266: 6004: 3315:

prompts for particular tasks, particularly with respect to how many examples of solved tasks are adjoined to the prompt (i.e. the value of

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Raffel, Colin; Shazeer, Noam; Roberts, Adam; Lee, Katherine; Narang, Sharan; Matena, Michael; Zhou, Yanqi; Li, Wei; Liu, Peter J. (2020).

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Zhang, Hang; Li, Xin; Bing, Lidong (2023-06-01). "Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding".

6846: 2621: 11338: 9391: 9018: 8924:

Zellers, Rowan; Holtzman, Ari; Bisk, Yonatan; Farhadi, Ali; Choi, Yejin (2019). "HellaSwag: Can a Machine Really Finish Your Sentence?".

8536: 5165: 4970: 2333: 1471: 456: 10278: 8879:

Srivastava, Aarohi; et al. (2022). "Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models".

6056: 5877: 3056:{\displaystyle \log({\text{Perplexity}})=-{\frac {1}{N}}\sum _{i=1}^{N}\log(\Pr({\text{token}}_{i}\mid {\text{context for token}}_{i}))} 1537:(MoE) can be applied, a line of research pursued by Google researchers since 2017 to train models reaching up to 1 trillion parameters. 11537: 10981: 10451: 9279: 7290:

Wu, Yue; Prabhumoye, Shrimai; Min, So Yeon (24 May 2023). "SPRING: GPT-4 Out-performs RL Algorithms by Studying Papers and Reasoning".

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Hoffmann, Jordan; Borgeaud, Sebastian; Mensch, Arthur; et al. (29 March 2022). "Training Compute-Optimal Large Language Models".

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Askell, Amanda; Bai, Yuntao; Chen, Anna; et al. (9 December 2021). "A General Language Assistant as a Laboratory for Alignment".

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Lee, Katherine; Ippolito, Daphne; Nystrom, Andrew; Zhang, Chiyuan; Eck, Douglas; Callison-Burch, Chris; Carlini, Nicholas (May 2022).

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For the training cost column, 1 petaFLOP-day = 1 petaFLOP/sec × 1 day = 8.64E19 FLOP. Also, only the largest model's cost is written.

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While quantized models are typically frozen, and only pre-quantized models are fine-tuned, quantized models can still be fine-tuned.

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Competing language models have for the most part been attempting to equal the GPT series, at least in terms of number of parameters.

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Bai, Yuntao; Kadavath, Saurav; Kundu, Sandipan; et al. (15 December 2022). "Constitutional AI: Harmlessness from AI Feedback".

8649:

Philosophy in the Flesh: The Embodied Mind and Its Challenge to Western Philosophy; Appendix: The Neural Theory of Language Paradigm

2538: 5795:

Bahdanau, Dzmitry; Cho, Kyunghyun; Bengio, Yoshua (2014). "Neural Machine Translation by Jointly Learning to Align and Translate".

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responses that lean towards particular political ideologies or viewpoints, depending on the prevalence of those views in the data.

1779:

to different parameters, with higher precision for particularly important parameters ("outlier weights"). See for a visual guide.

1626: 1010: 11848: 10512: 12843: 11986:

Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition

10127:"Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, the World's Largest and Most Powerful Generative Language Model" 6754: 6724: 5940: 2876: 1749:

for complex action sequences. The skills can be stored and later invoked, allowing increasing levels of abstraction in planning.

836: 11887: 10762: 10689: 9812: 7654: 7197: 6648: 1393:

As an example, consider a tokenizer based on byte-pair encoding. In the first step, all unique characters (including blanks and

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Antol, Stanislaw; Agrawal, Aishwarya; Lu, Jiasen; Mitchell, Margaret; Batra, Dhruv; Zitnick, C. Lawrence; Parikh, Devi (2015).

6958: 385: 11769: 7483: 9877: 9374: 8706: 8699:

Active Inference: The Free Energy Principle in Mind, Brain, and Behavior; Chapter 4 The Generative Models of Active Inference

8681: 8656: 5414: 3459:

gender norms. For example, it might associate nurses or secretaries predominantly with women and engineers or CEOs with men.

3393: 11740: 11427: 9844: 8204:

Schaeffer, Rylan; Miranda, Brando; Koyejo, Sanmi (2023-04-01). "Are Emergent Abilities of Large Language Models a Mirage?".

7632: 6879: 5756: 5442: 3350:

We see a fitness center sign. We then see a man talking to the camera and sitting and laying on a exercise ball. The man...

12884: 12584: 12275: 12111: 10424: 894: 657: 192: 11011: 1133:

Before 2017, there were a few language models that were large as compared to capacities then available. In the 1990s, the

12838: 11598: 9783: 9143: 9118: 8574: 5481:

Fathallah, Nadeen; Das, Arunav; De Giorgis, Stefano; Poltronieri, Andrea; Haase, Peter; Kovriguina, Liubov (2024-05-26).

2436: 2427: 1125: 912: 11456: 8604: 8131: 6621: 6185: 2807:", and it is not clear how they can perform linguistic tasks. There are several methods for understanding how LLM work. 1613:" (i.e. filling in the parts missing from the segment, the way "BERT" does it): for example, given a segment "I like to 12445: 11203: 3416: 2919: 1804:, etc. There have been many AI models trained specifically to ingest one modality and output another modality, such as 1253: 1041: 983: 745: 720: 669: 11367: 8026:

Hahn, Michael; Goyal, Navin (2023-03-14). "A Theory of Emergent In-Context Learning as Implicit Structure Induction".

5855: 2900: 12599: 12430: 11948: 10925: 10719: 9532: 9182: 7504:

Polino, Antonio; Pascanu, Razvan; Alistarh, Dan (2018-02-01). "Model compression via distillation and quantization".

6807: 6260: 1603: 1559:, although limited to the scope of a single conversation (more precisely, limited to the scope of a context window). 1142: 793: 788: 441: 11629: 4223:

38.5B tokens from webpages filtered for mathematical content and from papers submitted to the arXiv preprint server

12370: 11390: 9984: 8858: 6207: 4533:

363 billion token dataset based on Bloomberg's data sources, plus 345 billion tokens from general purpose datasets

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can use both text and image as inputs (although the vision component was not released to the public until GPT-4V);

1927:

model was fine-tuned into a multimodal model PaLM-E using the tokenization method, and applied to robotic control.

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Hubinger, Evan (10 January 2024). "Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training".

8391: 12787: 12440: 10573: 10482: 9732: 6569: 4492: 2849: 2451: 2430:, the lines change their slopes, appearing on a linear-log plot as a series of linear segments connected by arcs. 1746: 10891: 10618: 8726: 8072: 6900: 1931:

models have also been turned multimodal using the tokenization method, to allow image inputs, and video inputs.

1203:

with no offering of downloading the model to execute locally. But it was the 2022 consumer-facing browser-based

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Lin, Stephanie; Hilton, Jacob; Evans, Owain (2021). "TruthfulQA: Measuring How Models Mimic Human Falsehoods".

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Li, Yuanzhi; Bubeck, Sébastien; Eldan, Ronen; Del Giorno, Allie; Gunasekar, Suriya; Lee, Yin Tat (2023-09-11),

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A fine-tuned variant of GPT-3, termed GPT-3.5, was made available to the public through a web interface called

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that captured the imaginations of the general population and caused some media hype and online buzz. The 2023

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Kang, Daniel (2023). "Exploiting programmatic behavior of LLMs: Dual-use through standard security attacks".

8830: 8101: 5138: 3375: 2173:{\displaystyle {\begin{cases}C=C_{0}ND\\L={\frac {A}{N^{\alpha }}}+{\frac {B}{D^{\beta }}}+L_{0}\end{cases}}} 1703:

An LLM is a language model, which is not an agent as it has no goal, but it can be used as a component of an

821: 523: 299: 8851:"Sanitized open-source datasets for natural language and code understanding: how we evaluated our 70B model" 5508: 5482: 2852:

system": "Can one reasonably say that a system that passes exams for software engineering candidates is not

1645: 1433:. Even more widespread languages such as Portuguese and German have "a premium of 50%" compared to English. 12397: 11268: 7407: 3208: 2868: 2820: 2078: 1738: 1622: 1550: 1500: 1173: 778: 715: 625: 603: 446: 436: 2483:

argue that the emergent abilities are not unpredictably acquired, but predictably acquired according to a

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Jin, Charles; Rinard, Martin (2023-05-01). "Evidence of Meaning in Language Models Trained on Programs".

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per parameter to train on one token, whereas it costs 1 to 2 FLOPs per parameter to infer on one token.

994:, LLMs acquire these abilities by learning statistical relationships from vast amounts of text during a 12479: 12450: 12228: 11087:

UAE's Falcon 40B, World's Top-Ranked AI Model from Technology Innovation Institute, is Now Royalty-Free

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Nagel, Markus; Amjad, Rana Ali; Baalen, Mart Van; Louizos, Christos; Blankevoort, Tijmen (2020-11-21).

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Sharir, Or; Peleg, Barak; Shoham, Yoav (2020). "The Cost of Training NLP Models: A Concise Overview".

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The smaller models including 66B are publicly available, while the 175B model is available on request.

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Essentially GPT-3 but trained on a multi-lingual corpus (30% English excluding programming languages)

1680:

services, then the LLM can be fine-tuned to be able to read API documentation and call API correctly.

12322: 12175: 1994: 1809: 1226: 1165: 1006: 943: 549: 317: 187: 11237:"Google's newest A.I. model uses nearly five times more text data for training than its predecessor" 9695: 7960:

Caballero, Ethan; Gupta, Kshitij; Rish, Irina; Krueger, David (2022). "Broken Neural Scaling Laws".

1293: 12848: 12772: 12504: 12460: 12345: 12243: 10848: 7805:

Liu, Haotian; Li, Chunyuan; Wu, Qingyang; Lee, Yong Jae (2023-04-01). "Visual Instruction Tuning".

3543: 1249: 1161: 1116: 1013:, which enables efficient processing and generation of large-scale text data. Modern models can be 999: 995: 571: 491: 414: 332: 162: 124: 119: 79: 74: 11908: 11819: 9049: 3103:" depends on the specific type of LLM used. If the LLM is autoregressive, then "context for token 12752: 12722: 12389: 6154:

In other words, to express the same sentiment, some languages require up to 10 times more tokens.

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plot is a straight line (before it hits the plateau at zero), which does not look like emergence.

1942: 1924: 1730: 1581: 1101: 1069: 518: 367: 267: 94: 12223: 10483:"Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance" 6240: 5996: 2758: 2675: 2580: 1945:

is also multimodal. Mistral introduced its own multimodel Pixtral 12B model in September 2024.

1625:

loss is also used to stabilize training. However regularization loss is usually not used during

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do it): for example given a segment "I like to eat", the model predicts "ice cream", or "sushi".

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Proceedings of the 39th Annual Meeting on Association for Computational Linguistics - ACL '01

3551: 3362: 3293: 3289: 2892: 2296: 1883: 1304: 1216: 1177: 652: 474: 426: 282: 197: 69: 11529: 7242:"Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents" 3655:

An alternative to BERT; designed as encoder-only. Trained on 512 TPU v3 chips for 5.5 days.

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creators should exclude from their training data papers on creating or enhancing pathogens.

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Kaddour, Jean; et al. (2023). "Challenges and Applications of Large Language Models".

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This is the date that documentation describing the model's architecture was first released.

4502:

1 trillion tokens, from RefinedWeb (filtered web text corpus) plus some "curated corpora".

1745:. Instead of outputting individual actions, an LLM planner can also construct "skills", or 1134: 581: 531: 9050:"Near-Duplicate Sequence Search at Scale for Large Language Model Memorization Evaluation" 7450: 8: 12802: 12732: 12689: 12645: 12417: 12407: 12402: 12290: 9331:

Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models

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model, making it more expensive to train but cheaper to run inference compared to GPT-3.

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and thus not built to be prompted or generative. Training took 4 days on 64 TPUv2 chips.

2811: 2454:, unscrambling a word's letters, disambiguate word in context, converting spatial words, 2443: 1791: 1742: 1555:

Most results previously achievable only by (costly) fine-tuning, can be achieved through

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is an exponential curve (before it hits the plateau at one), which looks like emergence.

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For Transformer-based LLM, training cost is much higher than inference cost. It costs 6

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Hoffmann, Jordan; Borgeaud, Sebastian; Mensch, Arthur; Sifre, Laurent (12 April 2022).

10397: 10314: 10258: 10237: 10183: 10161: 10105: 10013: 9931: 9755: 9612: 9576: 9555: 9478: 9334: 9309: 9222: 9201: 9072: 8970: 8925: 8901: 8880: 8782: 8755: 8627: 8596: 8578: 8414: 8336: 8294: 8273: 8227: 8205: 8064: 8027: 8006: 7961: 7937: 7848: 7827: 7806: 7785: 7752: 7718: 7603: 7548: 7526: 7505: 7429: 7376: 7355: 7334: 7313: 7291: 7270: 7249: 7209: 7175: 7154: 7132: 7081: 7060: 7036: 7011: 6989: 6813: 6785: 6541: 6519: 6497: 6476: 6454: 6432: 6411: 6391: 6365:

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics

6336: 6289: 6175: 6108: 6082: 6027: 5847: 5829: 5796: 5564: 5530: 5028: 4933: 4093: 3944: 3412: 3204: 3166: 3146: 3126: 3106: 3086: 3066: 2880: 2827: 2513: 2493: 2484: 2455: 2263: 2239: 2215: 2187: 2026: 2001: 1972: 1959: 1863: 1843: 1823: 1813: 1688: 1556: 1546: 1534: 1528: 1388: 1289: 1018: 662: 586: 372: 167: 11561:"Mistral shocks AI community as latest open source model eclipses GPT-3.5 performance" 6778:"A Short Survey of Pre-trained Language Models for Conversational AI-A New Age in NLP" 6746: 6720: 6644: 3352:

a) demonstrates how to increase efficient exercise work by running up and down balls.

12817: 12529: 12337: 12248: 12089: 12077: 12057: 11879: 10685: 10673: 10645: 9804: 9622: 9370: 9076: 8974: 8962: 8702: 8677: 8652: 8600: 8372: 8068: 7869: 6817: 6803: 6777: 6613: 6256: 5851: 5730: 5680: 5612: 5534: 5320: 2910: 2904: 2864: 2471:(a combination of Hindi and English), and generating a similar English equivalent of 1715: 1704: 1408:-grams that most frequently occur together are then again merged into even lengthier 1297: 1148: 1061: 755: 598: 511: 307: 277: 222: 217: 172: 114: 9444: 9415: 6475:

Ryan (2022). "Training language models to follow instructions with human feedback".

6372: 4995:

Trained on real and synthetic "textbook-quality" data, for 14 days on 96 A100 GPUs.

4866:

chatbot. Grok-1 has a context length of 8,192 tokens and has access to X (Twitter).

12694: 12579: 12554: 12355: 12258: 12069: 11761: 10665: 10566:

Khrushchev, Mikhail; Vasilev, Ruslan; Petrov, Alexey; Zinov, Nikolay (2022-06-22),

9601:"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer" 9362: 9151: 9064: 8954: 8588: 8362: 8354: 8056: 7602:; Zettlemoyer, Luke (2023-05-01). "QLoRA: Efficient Finetuning of Quantized LLMs". 7475: 7241: 6795: 6368: 6248: 5839: 5722: 5670: 5639: 5604: 5520: 5278:

Trained for 1 epoch. Trained on 6144 H100 GPUs between December 2023 and May 2024.

4899: 4366: 4316: 3477: 3440: 1394: 1368: 1280: 1195:

at first deemed it too powerful to release publicly, out of fear of malicious use.

1073: 1030: 783: 536: 486: 396: 380: 350: 212: 207: 157: 147: 45: 11302:"Introducing Llama 2: The Next Generation of Our Open Source Large Language Model" 9869: 9361:. CI '23. New York, NY, USA: Association for Computing Machinery. pp. 12–24. 6166:

Petrov, Aleksandar; Malfa, Emanuele La; Torr, Philip; Bibi, Adel (June 23, 2023).

5406: 3764: 3399:

GPT-2-series models) as variously over 1% for exact duplicates or up to about 7%.

2019:(i.e. amount of neurons in its layers, amount of weights between them and biases), 1567:

on the bottom two rows, i.e. on "tired", which has been tokenized into two tokens.

1145:

in 2016. As it was before Transformers, it was done by seq2seq deep LSTM networks.

12806: 12767: 12762: 12630: 12360: 12233: 12208: 12190: 11732: 11419: 11093: 10349: 9836: 9354: 8159: 7624: 6871: 6357: 5044: 5010: 4881: 2748:{\displaystyle y={\text{average }}\Pr({\text{the most likely token is correct}})} 1938: 1692: 1533:

The largest LLM may be too expensive to train and use directly. For such models,

811: 615: 481: 421: 10418: 8435:"ChatGPT is more like an 'alien intelligence' than a human brain, says futurist" 8055:. Minneapolis, Minnesota: Association for Computational Linguistics: 1267–1273. 6252: 6247:. Artificial Intelligence: Foundations, Theory, and Algorithms. pp. 19–78. 6026:

Peng, Bo; et al. (2023). "RWKV: Reinventing RNNS for the Transformer Era".

2446:

from example demonstrations. In-context learning is involved in tasks, such as:

1503:, is used to further fine-tune a model based on a dataset of human preferences. 12514: 12494: 12218: 12073: 10669: 8958: 6930: 6670: 5748: 5710: 5675: 5608: 4863: 4832:

Used in Claude chatbot. Has a context window of 200,000 tokens, or ~500 pages.

4670: 4523: 4281: 4149: 3356:

c) then plays the ball and we see a graphics and hedge trimming demonstration.

3212: 1801: 1464: 1445: 1285: 1242: 1077: 991: 831: 362: 99: 12103: 11949:"llama-models/models/llama3_1/MODEL_CARD.md at main · meta-llama/llama-models" 11590: 10849:"Introducing LLaMA: A foundational, 65-billion-parameter large language model" 10367:"LaMDA: Towards Safe, Grounded, and High-Quality Dialog Models for Everything" 9775: 8005:

Bowman, Samuel R. (2023). "Eight Things to Know about Large Language Models".

3190:

to their training data, models are usually evaluated by their perplexity on a

1540: 1499:

Reinforcement learning from human feedback (RLHF) through algorithms, such as

1215:

capabilities. OpenAI did not reveal high-level architecture and the number of

979: 12868: 12777: 12589: 12569: 12350: 12081: 11448: 9626: 8559: 8123: 7240:

Huang, Wenlong; Abbeel, Pieter; Pathak, Deepak; Mordatch, Igor (2022-06-28).

6617: 6605: 6167: 5734: 5684: 5631: 5616: 5233: 4089: 4056: 3381: 3276: 2896: 2872: 2063: 2056: 1512: 1426: 750: 679: 561: 292: 177: 10279:"Language modelling at scale: Gopher, ethical considerations, and retrieval" 10068:"GPT-J-6B: An Introduction to the Largest Open Source GPT Model | Forefront" 9366: 9155: 8358: 6799: 6431:

Brown, Tom B.; et al. (2020). "Language Models are Few-Shot Learners".

5643: 3419:, showed that there are potential security risks in language models such as 2830:. The resulting models were reverse-engineered, and it turned out they used 1425:

up to 15 times more tokens per word for some languages, for example for the

1176:

mechanism developed by Bahdanau et al. in 2014. The following year in 2018,

12757: 12375: 11980: 11359: 11174:"Tel Aviv startup rolls out new advanced AI language model to rival OpenAI" 11062: 10677: 10417:

Black, Sidney; Biderman, Stella; Hallahan, Eric; et al. (2022-05-01).

8966: 8376: 8060: 7599: 5882: 4546: 4247: 3365:

selects b) as the most likely completion, though the correct answer is d).

3211:

is intricately linked to perplexity, a relationship notably established by

2915: 2458:(for example, replying "northeast" upon ), color terms represented in text. 2060: 1772: 1562: 1376: 1029:

inherent in human language corpora, but they also inherit inaccuracies and

11993:

Zhao, Wayne Xin; et al. (2023). "A Survey of Large Language Models".

10982:"Cerebras-GPT: A Family of Open, Compute-efficient, Large Language Models" 10912: 10711: 10160:

Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model".

9528: 7981: 5878:"New AI fake text generator may be too dangerous to release, say creators" 5817: 2422: 1474:

series of LLMs is trained on textbook-like data generated by another LLM.

12714: 12594: 12307: 12200: 12148: 11621: 9600: 8802: 5843: 5752: 5638:. Morristown, NJ, USA: Association for Computational Linguistics: 26–33. 5525: 5194: 4769: 4741: 4541:

Trained on financial data from proprietary sources, for financial tasks.

3542:

First GPT model, decoder-only transformer. Trained for 30 days on 8 P600

1637:

Substantial infrastructure is necessary for training the largest models.

1089: 556: 50: 10452:"An empirical analysis of compute-optimal large language model training" 9980: 8850: 7545: 5726: 5632:"Scaling to very very large corpora for natural language disambiguation" 2939:

The most commonly used measure of a language model's performance is its

1800:

refers to a type of input or output, such as video, image, audio, text,

12317: 12038: 10619:"Minerva: Solving Quantitative Reasoning Problems with Language Models" 9243: 5484:

NeOn-GPT: A Large Language Model-Powered Pipeline for Ontology Learning

5101: 4949: 4915: 4785: 4341: 4032: 3782: 3745: 2940: 2282: 1610: 1238: 1105: 705: 401: 327: 10567: 9724: 8500:"Why an Octopus-like Creature Has Come to Symbolize the State of A.I." 1436:

Greedy tokenization also causes subtle problems with text completion.

12185: 10883: 9936: 9581: 9483: 9314: 8878: 7030: 6782:

Proceedings of the Australasian Computer Science Week Multiconference

5105: 4617: 3889: 3871: 3814: 3411:

A study by researchers at Google and several universities, including

2888: 2804: 2472: 2022:

size of its (pre-)training dataset (i.e. number of tokens in corpus,

1412:-gram, until a vocabulary of prescribed size is obtained (in case of 1211:

was praised for its increased accuracy and as a "holy grail" for its

1097: 864: 645: 11718: 11478: 10791: 10203: 9068: 8592: 8048: 7629:

Proceedings of the 31st International Conference on Machine Learning

7480:

Proceedings of the 37th International Conference on Machine Learning

7246:

Proceedings of the 39th International Conference on Machine Learning

6901:"metaseq/projects/OPT/chronicles at main · facebookresearch/metaseq" 2914:, British cognitive linguist and digital communication technologist 2665:{\displaystyle y={\text{average }}\log(\Pr({\text{correct token}}))} 1707:. Researchers have described several methods for such integrations. 1488: 12660: 12640: 12625: 12604: 12574: 12519: 12484: 12365: 12029: 12014: 11999: 11157: 11135: 11113: 11047: 10798: 10747: 10603: 10548: 10513:"Democratizing access to large-scale language models with OPT-175B" 10402: 10319: 10263: 10242: 10188: 10166: 10110: 10038:"GPT-3's free alternative GPT-Neo is something to be excited about" 10018: 9951: 9760: 9617: 9560: 9339: 9227: 9206: 9119:"AI chatbots have been used to create dozens of news content farms" 8930: 8906: 8885: 8787: 8760: 8632: 8583: 8419: 8341: 8299: 8278: 8247: 8232: 8210: 8032: 8011: 7966: 7942: 7853: 7832: 7811: 7790: 7757: 7723: 7608: 7553: 7531: 7510: 7434: 7400:"Voyager | An Open-Ended Embodied Agent with Large Language Models" 7381: 7360: 7339: 7318: 7296: 7275: 7254: 7214: 7180: 7159: 7137: 7086: 7065: 7041: 7016: 6994: 6872:"From bare metal to a 70B model: infrastructure set-up and scripts" 6790: 6546: 6524: 6502: 6481: 6459: 6437: 6416: 6396: 6341: 6294: 6180: 6113: 6104:

What do tokens know about their characters and how do they know it?

6102: 6087: 6032: 5913: 5834: 5569: 4457: 4441: 4066: 3960: 3191: 3083:

is the number of tokens in the text corpus, and "context for token

2903:

for using language as a model of learning tasks and understanding.

2857: 2468: 2404:{\displaystyle \alpha =0.34,\beta =0.28,A=406.4,B=410.7,L_{0}=1.69} 1602:

autoregressive (i.e. predicting how the segment continues, the way

12058:"Baby steps in evaluating the capacities of large language models" 11937:"The Llama 3 Herd of Models" (July 23, 2024) Llama Team, AI @ Meta 9898: 9666: 9183:"How Googlers cracked an SF rival's tech model with a single word" 8824: 8093: 7597: 7198:"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" 6776:

Zaib, Munazza; Sheng, Quan Z.; Emma Zhang, Wei (4 February 2020).

6168:"Language Model Tokenizers Introduce Unfairness Between Languages" 5941:"GPT-4 is bigger and better than ChatGPT—but OpenAI won't say why" 5801: 1313:

For example, the BPE tokenizer used by GPT-3 (Legacy) would split

12797: 12655: 12635: 12509: 12253: 12168: 10312: 7934: 7655:"ImageNet Classification with Deep Convolutional Neural Networks" 6931:"State of the Art: Training >70B LLMs on 10,000 H100 clusters" 6671:"The Illustrated GPT-2 (Visualizing Transformer Language Models)" 5480: 5134: 4385: 3729: 3420: 3187: 2815: 1805: 1588: 1430: 1204: 1200: 1199:

in 2020 went a step further and as of 2024 is available only via

1169: 1157: 1093: 1057: 1045: 640: 11149: 10510: 7399: 7129: 7008: 6079:

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

12163: 12158: 11853: 11129:

Parameter Language Model with Sparse Heterogeneous Computing".

10920: 10763:"20B-parameter Alexa model sets new marks in few-shot learning" 8411: 7427: 6311:"The Art of Prompt Design: Prompt Boundaries and Token Healing" 6287: 6215: 5751:; Shazeer, Noam; Parmar, Niki; Uszkoreit, Jakob; Jones, Llion; 5259: 4556: 4184: 3839:

Standard architecture but trained on a supercomputing cluster.

3818: 3670: 3633: 3561: 3524: 3257:{\displaystyle {\text{Entropy}}=\log _{2}({\text{Perplexity}})} 1398: 1241:'s models Mistral 7B and Mixtral 8x7b have the more permissive 1192: 1065: 1053: 1037: 1005:

The largest and most capable LLMs, as of August 2024, are

391: 10394: 8047:

Pilehvar, Mohammad Taher; Camacho-Collados, Jose (June 2019).

7653:

Krizhevsky, Alex; Sutskever, Ilya; Hinton, Geoffrey E (2012).

7623:

Kiros, Ryan; Salakhutdinov, Ruslan; Zemel, Rich (2014-06-18).

7524: 7476:"Up or Down? Adaptive Rounding for Post-Training Quantization" 7451:"How to run an LLM locally on your PC in less than 10 minutes" 3354:

b) moves all his arms and legs and builds up a lot of muscle.

2826:

In another example, the authors trained small transformers on

2570:{\displaystyle y={\text{average }}\Pr({\text{correct token}})} 1270:

List of datasets for machine-learning research § Internet

1229:

models have been gaining popularity, especially at first with

12853: 12489: 11301: 11039: 9416:"Improving language understanding with unsupervised learning" 9392:"AI language models are rife with different political biases" 8325:"The debate over understanding in AI's large language models" 7101: 7058: 6516: 5822:

Transactions of the Association for Computational Linguistics

5747: 5709:

Halevy, Alon; Norvig, Peter; Pereira, Fernando (March 2009).

5490:. Extended Semantic Web Conference 2024. Hersonissos, Greece. 4586: 4415: 4374: 3987: 3852: 3772: 3698: 3660: 3623: 3514: 2206: 1934: 1928: 1660: 1573: 1413: 1234: 1208: 1196: 1188: 1184: 1081: 1049: 635: 630: 357: 10595: 10565: 10542:

2022). "OPT: Open Pre-trained Transformer Language Models".

6139:

Language models cost much more in some languages than others

1616:

cream", the model predicts that "eat" and "ice" are missing.

1572:

weights. For example, the small (i.e. 117M parameter sized)

1371:

the datasets. Because LLMs generally require input to be an

11504: 11391:"Building AI for business: IBM's Granite foundation models" 11241: 10739: 10449: 10158: 9500:"Cerebras Shifts Architecture To Meet Massive AI/ML Models" 9244:"Encryption Based Covert Channel for Large Language Models" 8462: 8460: 8248:"Large Language Model: world models or surface statistics?" 8175:"The Unpredictable Abilities Emerging From Large AI Models" 6959:"The emerging types of language models and why they matter" 5093: 5031:(MoE) architecture. Context window above 1 million tokens. 4845: 4638: 4101: 3308: 2166: 1714:, a portmanteau of "Reason + Act", constructs an 1541:

Prompt engineering, attention mechanism, and context window

10420:

GPT-NeoX-20B: An Open-Source Autoregressive Language Model

9280:"ChatGPT Replicates Gender Bias in Recommendation Letters" 8989:"Your job is (probably) safe from artificial intelligence" 8923: 7911:"Mistral releases Pixtral 12B, its first multimodal model" 7332: 6358:"Deduplicating Training Data Makes Language Models Better" 5818:"A Primer in BERTology: What We Know About How BERT Works" 5591:

Kilgarriff, Adam; Grefenstette, Gregory (September 2003).

4088:

Reduced-parameter model trained on more data. Used in the

2059:") for LLM autoregressively trained for one epoch, with a 12650: 11012:"Abu Dhabi-based TII launches its own version of ChatGPT" 9359:

Proceedings of the ACM Collective Intelligence Conference

8153:

A Closer Look at Large Language Models Emergent Abilities

7959: 7741:"Flamingo: a Visual Language Model for Few-Shot Learning" 7652: 7239: 6025: 5228: 5204: 5199: 5170: 5039: 5005: 4975: 4944: 4936:

model, with 12.9 billion parameters activated per token.

4910: 4876: 4840: 4812: 4780: 4751: 4746: 4716: 4681: 4644: 4612: 4581: 4551: 4518: 4487: 4452: 4420: 4380: 4346: 4311: 4276: 4241: 4210: 4179: 4144: 4107: 4061: 4027: 3993: 3955: 3918: 3884: 3847: 3809: 3777: 3740: 3703: 3665: 3628: 3599: 3556: 3519: 2285:/token), achieved by the trained LLM on the test dataset. 1964:

The following four hyper-parameters characterize an LLM:

1677: 1511:

Using "self-instruct" approaches, LLMs have been able to

1085: 9048:

Peng, Zhencan; Wang, Zhizhi; Deng, Dong (13 June 2023).

8779: 8753: 8560:"Survey of Hallucination in Natural Language Generation" 8457: 8392:"Microsoft Says New A.I. Shows Signs of Human Reasoning" 8318: 8316: 8314: 8312: 8310: 8046: 5593:"Introduction to the Special Issue on the Web as Corpus" 4932:

Outperforms GPT-3.5 and Llama 2 70B on many benchmarks.

4202:

English-Russian model based on Microsoft's Megatron-LM.

3326: 3283: 923:

List of datasets in computer vision and image processing

10540: 10010: 9598: 9574: 9476: 9353:

Kotek, Hadas; Dockum, Rikker; Sun, David (2023-11-05).

9329:

Cheng, Myra; Durmus, Esin; Jurafsky, Dan (2023-05-29),

8323:

Mitchell, Melanie; Krakauer, David C. (28 March 2023).

8124:"Mapping Language Models to Grounded Conceptual Spaces" 7622: 7473: 7374: 7194: 6388:

Textbooks Are All You Need II: phi-1.5 technical report

6385: 6355: 5971:"Parameters in notable artificial intelligence systems" 5815: 4171:

GPT-3 architecture with some adaptations from Megatron

2281:

is the average negative log-likelihood loss per token (

1796:

Multimodality means "having several modalities", and a

1775:

per layer. Further improvement can be done by applying

1288:

is associated to the integer index. Algorithms include

12039:"AI Index Report 2024 – Artificial Intelligence Index" 11655:"Phi-2: The surprising power of small language models" 10481:

Narang, Sharan; Chowdhery, Aakanksha (April 4, 2022).

10365:

Cheng, Heng-Tze; Thoppilan, Romal (January 21, 2022).

10256: 9696:"Pretrained models — transformers 2.0.0 documentation" 9553: 9355:"Gender bias and stereotypes in Large Language Models" 8292: 8203: 7737: 7686: 7173: 6333: 5816:

Rogers, Anna; Kovaleva, Olga; Rumshisky, Anna (2020).

5590: 4694: 4657: 4561: 4395: 4326: 4076: 4019:

Specialized for response generation in conversations.

3931: 3926: 2055:, called "scaling laws". One particular scaling law (" 10952:"GPT-4 architecture, datasets, costs and more leaked" 10884:"The Falcon has landed in the Hugging Face ecosystem" 10416: 9928: 9837:"OpenAI's GPT-3 Language Model: A Technical Overview" 9144:"Could chatbots help devise the next pandemic virus?" 8551: 8307: 7782: 7503: 6602: 6430: 5439: 4652: 4291: 4286: 4257: 4252: 4218: 4189: 4159: 4154: 4120: 4115: 4007: 4001: 3970: 3965: 3899: 3857: 3828: 3823: 3716: 3711: 3618:

Base model for many Google projects, such as Imagen.

3275:

In the evaluation and comparison of language models,

3221: 3169: 3149: 3129: 3109: 3089: 3069: 2950: 2761: 2719: 2678: 2624: 2583: 2541: 2516: 2496: 2336: 2299: 2266: 2242: 2218: 2190: 2072: 2029: 2004: 1975: 1886: 1866: 1846: 1826: 1237:, though both have restrictions on the field of use. 11204:"With Bedrock, Amazon enters the generative AI race" 11105: 9753: 8625: 8407: 8405: 6473: 6451: 5264: 5110: 4689: 4591: 4528: 4497: 4462: 4406:

Corpus has 20 languages. "Overtrained" (compared to

4390: 4321: 4071: 4037: 3910:

Fine-tuned for desirable behavior in conversations.

3894: 3681: 10445: 10443: 10441: 10102: 9328: 6775: 6489: 6165: 5708: 5435: 5433: 5431: 5156:The largest model ever trained on CPU-only, on the 5144: 4790: 3787: 3750: 3675: 3643: 3571: 3358:d) performs sit ups while on the ball and talking. 3215:. This relationship is mathematically expressed as 1316:

tokenizer: texts -> series of numerical "tokens"

10235: 10154: 10152: 9952:"ChatGPT: Optimizing Language Models for Dialogue" 8945:"Prepare for truly useful large language models". 8224: 7353: 6836:Jurafsky, Dan; Martin, James H. (7 January 2023). 6495: 5189:Microsoft markets them as "small language model". 3638: 3566: 3529: 3256: 3175: 3155: 3135: 3115: 3095: 3075: 3055: 2911:The Language Myth: Why Language Is Not An Instinct 2785: 2747: 2702: 2664: 2607: 2569: 2522: 2502: 2403: 2318: 2272: 2248: 2224: 2196: 2172: 2035: 2010: 1981: 1910: 1872: 1852: 1832: 1321: 11412: 11296: 11294: 11230: 11228: 10591: 10589: 9642:google-research/text-to-text-transfer-transformer 8402: 8049:"Proceedings of the 2019 Conference of the North" 7982:"137 emergent abilities of large language models" 7745:Advances in Neural Information Processing Systems 7659:Advances in Neural Information Processing Systems 7202:Advances in Neural Information Processing Systems 7079: 7053: 7051: 6987: 6538: 6467: 6245:Foundation Models for Natural Language Processing 6135:"All languages are NOT created (tokenized) equal" 5794: 5764:Advances in Neural Information Processing Systems 5657:Resnik, Philip; Smith, Noah A. (September 2003). 5450:Advances in Neural Information Processing Systems 2837: 1489:Reinforcement learning from human feedback (RLHF) 1191:in 2019 that caught widespread attention because 12866: 12336: 11331:"llama/MODEL_CARD.md at main · meta-llama/llama" 11127: 10438: 8322: 6662: 6598: 6596: 6594: 5428: 5088:Includes three models, Haiku, Sonnet, and Opus. 4898:Multimodal model, comes in three sizes. Used in 4410:) for better performance with fewer parameters. 4365:A language model designed for live-streaming on 4336:bidirectional sequence-to-sequence architecture 3143:. If the LLM is masked, then "context for token 3123:" is the segment of text appearing before token 3014: 2731: 2645: 2553: 2442:The most intriguing among emergent abilities is 1761:outside the range of most consumer electronics. 12133: 11151:Democratizing Large Language Model Alignment". 10480: 10149: 9805:"Better language models and their implications" 9180: 8329:Proceedings of the National Academy of Sciences 7847:OpenAI (2023-03-27). "GPT-4 Technical Report". 7716: 7289: 6238: 6100: 5407:"Better Language Models and Their Implications" 2899:presented Neural Theory of Language (NTL) as a 2793:is a step-function, which looks like emergence. 2467:identifying offensive content in paragraphs of 11291: 11225: 10843: 10841: 10839: 10637: 10586: 10364: 10360: 10358: 10231: 10229: 10180: 10031: 10029: 9831: 9829: 9472: 9470: 9352: 8899: 8557: 7048: 6835: 6570:"More Efficient In-Context Learning with GLaM" 5509:"Human Language Understanding & Reasoning" 5499: 5497: 1168:". This paper's goal was to improve upon 2014 1160:conference, Google researchers introduced the 918:List of datasets for machine-learning research 12119: 10561: 10559: 10410: 10343:PaLM: Scaling Language Modeling with Pathways 10125:Alvi, Ali; Kharya, Paresh (11 October 2021). 9944: 9307: 9277: 8919: 8917: 8781:(2023). "A Survey of Large Language Models". 8019: 7151: 6606:"Emergent Abilities of Large Language Models" 6591: 6409: 6101:Kaushal, Ayush; Mahowald, Kyle (2022-06-06), 4440:Available for ChatGPT Plus users and used in 3387: 951: 11880:"nvidia/Nemotron-4-340B-Base · Hugging Face" 10643: 9104: 9057:Proceedings of the ACM on Management of Data 9047: 9019:"Generative AI Could Raise Global GDP by 7%" 8121: 7310: 6563: 6561: 6559: 6557: 5755:; Kaiser, Łukasz; Polosukhin, Illia (2017). 2257:is the number of tokens in the training set. 1644: 1477: 12022: 11733:"Introducing the next generation of Claude" 10836: 10355: 10308: 10306: 10304: 10226: 10026: 9826: 9768: 9467: 8893: 7825: 7268: 6636: 6568:Dai, Andrew M; Du, Nan (December 9, 2021). 6234: 6232: 5656: 5494: 4303:Trained on scientific text and modalities. 3982:Later developed into the Chinchilla model. 3163:" is the segment of text surrounding token 1296:. There are also special tokens serving as 12126: 12112: 11992: 11762:"Fugaku-LLM/Fugaku-LLM-13B · Hugging Face" 10556: 10511:Susan Zhang; Mona Diab; Luke Zettlemoyer. 10474: 10124: 9408: 9278:Stokel-Walker, Chris (November 22, 2023). 8914: 8775: 8773: 8771: 8727:"Evaluation Metrics for Language Modeling" 8651:. New York Basic Books. pp. 569–583. 8122:Patel, Roma; Pavlick, Ellie (2021-10-06). 7804: 6952: 6950: 6132: 5629: 3577: 3342:, even though this is not literally true. 1495:Reinforcement learning from human feedback 958: 944: 12028: 12013: 11998: 11156: 11134: 11112: 11046: 10949: 10905: 10797: 10746: 10602: 10547: 10401: 10318: 10262: 10241: 10187: 10165: 10109: 10017: 9935: 9870:"openai-community/gpt2-xl · Hugging Face" 9759: 9616: 9580: 9559: 9521: 9482: 9338: 9313: 9226: 9205: 8929: 8905: 8884: 8872: 8786: 8759: 8749: 8747: 8720: 8718: 8631: 8582: 8418: 8366: 8340: 8298: 8277: 8271: 8231: 8209: 8031: 8025: 8010: 7965: 7941: 7852: 7831: 7810: 7789: 7756: 7722: 7607: 7552: 7530: 7509: 7433: 7380: 7359: 7338: 7317: 7295: 7274: 7253: 7213: 7179: 7158: 7136: 7085: 7064: 7040: 7033:Artificial Intelligence Index Report 2023 7015: 6993: 6789: 6610:Transactions on Machine Learning Research 6554: 6545: 6523: 6501: 6480: 6458: 6436: 6415: 6395: 6340: 6293: 6239:Paaß, Gerhard; Giesselbach, Sven (2022). 6229: 6179: 6112: 6086: 6031: 5833: 5800: 5674: 5568: 5524: 5248:Multiple sizes, the smallest being 0.5B. 3990:(Language Models for Dialog Applications) 3585:An early and influential language model. 2803:Large language models by themselves are " 2289:and the statistical hyper-parameters are 2233:is the number of parameters in the model. 1033:present in the data they are trained on. 10301: 10006: 10004: 10002: 9973: 9667:"Imagen: Text-to-Image Diffusion Models" 9497: 9389: 9199: 7955: 7953: 7936:Compute-Optimal Large Language Models". 7567: 6928: 6308: 5711:"The Unreasonable Effectiveness of Data" 2421: 1561: 1263: 1172:technology, and was based mainly on the 1147: 1124: 1115: 12007: 11685:"Our next-generation model: Gemini 1.5" 11558: 11201: 11004: 9864: 9862: 9498:Prickett, Nicole Hemsoth (2021-08-24). 8768: 8696: 8529:"The A to Z of Artificial Intelligence" 8466: 7908: 6956: 6947: 6721:"Long context prompting for Claude 2.1" 6691:"Our next-generation model: Gemini 1.5" 6046: 5558: 5503: 5443:"Language Models are Few-Shot Learners" 3288:A large number of testing datasets and 1840:. Make a small multilayered perceptron 1017:for specific tasks or can be guided by 18:Large language model emergent abilities 14: 12867: 11711: 11652: 11648: 11646: 11388: 11261: 11143: 11121: 11099: 11080: 11055: 11033: 10973: 10878: 10876: 10874: 10806: 10785: 10755: 10733: 10704: 10611: 10534: 10504: 10388: 10336: 10334: 10332: 10330: 10271: 10250: 10196: 10174: 10118: 10098: 10096: 10094: 10092: 10060: 9891: 9797: 9747: 9717: 9594: 9592: 9547: 9491: 9273: 9271: 9189:from the original on 16 December 2023. 9136: 9110: 9011: 8981: 8938: 8744: 8715: 8646: 8521: 8491: 8469:"What Kind of Mind Does ChatGPT Have?" 8427: 8383: 8286: 8265: 8240: 8218: 8004: 7998: 7885: 7867: 7846: 7208:. Curran Associates, Inc.: 9459–9474. 7002: 6188:from the original on December 15, 2023 5938: 5561:A Bit of Progress in Language Modeling 5456:. Curran Associates, Inc.: 1877–1901. 5304:405B version took 31 million hours on 3693:Trained on 32 TPUv3 chips for 1 week. 2875:, a phenomenon which has been termed " 2510:be the number of parameter count, and 2205:is the cost of training the model, in 12107: 12055: 11695:from the original on 16 February 2024 11665:from the original on 12 December 2023 11601:from the original on 13 February 2024 11571:from the original on 11 December 2023 11459:from the original on 15 December 2023 11370:from the original on 15 December 2023 11234: 9999: 9535:from the original on January 13, 2021 9437: 8724: 8671: 8497: 8197: 8172: 8166: 8145: 8115: 8086: 8040: 7974: 7950: 7928: 7591: 7539: 7518: 7497: 7467: 7421: 7394: 7392: 7368: 7347: 7326: 7283: 7262: 7233: 7188: 7167: 7145: 7123: 7094: 7073: 6981: 6831: 6829: 6827: 6769: 6757:from the original on February 2, 2024 6701:from the original on 18 February 2024 6567: 6532: 6510: 6424: 6327: 6200: 6076: 5920:from the original on January 14, 2024 5890:from the original on 14 February 2019 3394:Artificial intelligence and copyright 3339:you can't teach an old dog new tricks 3327:Adversarially constructed evaluations 3284:Task-specific datasets and benchmarks 2810:Mechanistic interpretability aims to 2415: 1997:itself, such as number of parameters 1580:The largest models, such as Google's 1522: 1506: 12585:Simple Knowledge Organization System 11540:from the original on 8 December 2023 10950:Schreiner, Maximilian (2023-07-11). 10035: 9859: 9605:Journal of Machine Learning Research 9568: 9390:Heikkilä, Melissa (August 7, 2023). 9241: 9220: 9116: 8389: 7102:"PAL: Program-aided Language Models" 6727:from the original on August 27, 2024 5875: 5630:Banko, Michele; Brill, Eric (2001). 4428:Unknown (According to rumors: 1760) 3296:, and mathematical problem-solving. 3198: 2891:have been developed in the field of 1739:propose increasingly difficult tasks 11653:Hughes, Alyssa (12 December 2023). 11643: 11622:"Cheaper, Better, Faster, Stronger" 10992:from the original on March 28, 2023 10979: 10931:from the original on March 14, 2023 10871: 10644:Ananthaswamy, Anil (8 March 2023). 10327: 10089: 9987:from the original on March 12, 2023 9921: 9735:from the original on 2 January 2024 9589: 9301: 9268: 8575:Association for Computing Machinery 8185:from the original on March 16, 2023 7909:Wiggers, Kyle (11 September 2024). 7625:"Multimodal Neural Language Models" 6668: 6642: 6077:Gu, Albert; Dao, Tri (2023-12-01), 5951:from the original on March 17, 2023 4051:based on the Megatron architecture 3765:a series of free GPT-3 alternatives 3434: 2450:reported arithmetics, decoding the 1439: 1397:) are treated as an initial set of 913:Glossary of artificial intelligence 24: 12056:Frank, Michael C. (27 June 2023). 11974: 11619: 11559:Franzen, Carl (11 December 2023). 11171: 11063:"tiiuae/falcon-40b · Hugging Face" 10824:from the original on 13 March 2023 10773:from the original on 15 March 2023 10692:from the original on 16 March 2023 10462:from the original on 13 April 2022 10289:from the original on 20 March 2023 10214:from the original on 16 March 2023 10137:from the original on 13 March 2023 9909:from the original on 11 March 2023 9847:from the original on 27 March 2023 8607:from the original on 26 March 2023 8094:"WiC: The Word-in-Context Dataset" 7598:Dettmers, Tim; Pagnoni, Artidoro; 7574:newsletter.maartengrootendorst.com 7389: 6969:from the original on 16 March 2023 6852:from the original on 23 March 2023 6824: 6624:from the original on 22 March 2023 6269:from the original on 3 August 2023 6007:from the original on June 10, 2024 4604:Trained on crowdsourced open data 3417:University of California, Berkeley 2926:and generate human like language. 2920:probabilistic context-free grammar 1687:: the augmentation of an LLM with 25: 12896: 12600:Thesaurus (information retrieval) 11279:from the original on May 18, 2023 10859:from the original on 3 March 2023 10712:"bigscience/bloom · Hugging Face" 10646:"In AI, is bigger always better?" 10048:from the original on 9 March 2023 9256:from the original on 24 June 2024 9162:from the original on 18 June 2023 9029:from the original on 18 June 2023 8999:from the original on 17 June 2023 8539:from the original on 16 June 2023 8479:from the original on 12 June 2023 8445:from the original on 12 June 2023 8173:Ornes, Stephen (March 16, 2023). 5997:"LMSYS Chatbot Arena Leaderboard" 5399: 4141:OPT (Open Pretrained Transformer) 3462: 2798: 2530:be the performance of the model. 2047:performance after (pre-)training. 1632: 1458: 1303:for masked-out token (as used in 1025:regarding syntax, semantics, and 27:Type of artificial neural network 11941: 11930: 11901: 11872: 11841: 11812: 11783: 11754: 11725: 11677: 11613: 11583: 11552: 11522: 11497: 11471: 11441: 11382: 11352: 11323: 11249:from the original on 16 May 2023 11195: 11165: 10943: 9688: 9659: 9633: 9455:from the original on 19 May 2023 9383: 9346: 9322: 9235: 9214: 9193: 9174: 9098: 9041: 8843: 8817: 8795: 8725:Huyen, Chip (October 18, 2019). 8690: 8665: 8640: 8619: 8509:from the original on 30 May 2023 7689:"VQA: Visual Question Answering" 7570:"A Visual Guide to Quantization" 7448: 5378: 5369: 5360: 5351: 5342: 5333: 3915:GLaM (Generalist Language Model) 2739:the most likely token is correct 1785: 1640: 11959:from the original on 2024-07-23 11919:from the original on 2024-06-15 11890:from the original on 2024-06-15 11861:from the original on 2024-06-17 11830:from the original on 2024-05-13 11801:from the original on 2024-04-27 11772:from the original on 2024-05-17 11743:from the original on 2024-03-04 11632:from the original on 2024-05-05 11487:from the original on 2024-05-28 11430:from the original on 2024-01-06 11401:from the original on 2024-07-22 11341:from the original on 2024-05-28 11312:from the original on 2024-01-05 11235:Elias, Jennifer (16 May 2023). 11214:from the original on 2023-07-24 11184:from the original on 2023-07-24 11022:from the original on 2023-04-03 10962:from the original on 2023-07-12 10894:from the original on 2023-06-20 10722:from the original on 2023-04-12 10576:from the original on 2023-06-16 10523:from the original on 2023-03-12 10493:from the original on 2022-04-04 10427:from the original on 2022-12-10 10377:from the original on 2022-03-25 9962:from the original on 2022-11-30 9880:from the original on 2024-07-24 9815:from the original on 2023-03-16 9786:from the original on 2019-11-14 9706:from the original on 2024-08-05 9677:from the original on 2024-03-27 9649:from the original on 2024-03-29 9645:, Google Research, 2024-04-02, 9510:from the original on 2023-06-20 9426:from the original on 2023-03-18 9290:from the original on 2023-12-29 9086:from the original on 2024-08-27 8861:from the original on 2024-07-26 8833:from the original on 2024-05-08 8134:from the original on 2023-06-24 8104:from the original on 2023-06-27 8075:from the original on 2023-06-27 7902: 7889:Google Keynote (Google I/O '23) 7879: 7861: 7840: 7819: 7798: 7776: 7765:from the original on 2023-07-02 7731: 7710: 7699:from the original on 2023-07-02 7680: 7669:from the original on 2023-07-02 7646: 7635:from the original on 2023-07-02 7616: 7561: 7486:from the original on 2023-06-14 7442: 7410:from the original on 2023-06-08 7304: 7222:from the original on 2023-06-12 7112:from the original on 2023-06-12 7024: 6957:Wiggers, Kyle (28 April 2022). 6922: 6911:from the original on 2024-01-24 6893: 6882:from the original on 2024-07-26 6864: 6739: 6713: 6683: 6651:from the original on 2023-07-25 6580:from the original on 2023-03-12 6445: 6403: 6379: 6349: 6302: 6281: 6159: 6126: 6094: 6070: 6059:from the original on 2023-11-17 6040: 6019: 5989: 5963: 5939:Heaven, Will (March 14, 2023). 5932: 5902: 5876:Hern, Alex (14 February 2019). 5869: 5858:from the original on 2022-04-03 5809: 5788: 5777:from the original on 2024-02-21 5741: 5691:from the original on 2024-06-07 5541:from the original on 2023-11-17 5463:from the original on 2023-11-17 5417:from the original on 2020-12-19 4493:Technology Innovation Institute 3497:Number of parameters (billion) 3449: 3368: 2924:NLP to model cognitive patterns 2850:artificial general intelligence 2452:International Phonetic Alphabet 1953: 1274: 1187:was introduced in 2018, it was 1092:models initially released with 12181:Natural language understanding 10036:Iyer, Abhishek (15 May 2021). 9181:Stephen Council (1 Dec 2023). 8676:. Cambridge University Press. 8467:Newport, Cal (13 April 2023). 7886:Pichai, Sundar (10 May 2023), 6845:(3rd edition draft ed.). 6839:Speech and Language Processing 6309:Lundberg, Scott (2023-12-12). 6049:"What Is a Transformer Model?" 5702: 5659:"The Web as a Parallel Corpus" 5650: 5623: 5584: 5559:Goodman, Joshua (2001-08-09), 5552: 5474: 5123:Training cost 10 million USD. 5120:Databricks Open Model License 4377:(Large Language Model Meta AI) 4132:Trained for ~60 days on ~6000 3251: 3243: 3050: 3047: 3017: 3011: 2965: 2957: 2838:Understanding and intelligence 2780: 2762: 2742: 2734: 2697: 2679: 2659: 2656: 2648: 2642: 2602: 2584: 2564: 2556: 1905: 1902: 1896: 1890: 1755: 1685:retrieval-augmented generation 1683:A simpler form of tool use is 1484:Fine-tuning (machine learning) 1011:transformer-based architecture 333:Relevance vector machine (RVM) 13: 1: 12705:Optical character recognition 11389:Nirmal, Dinesh (2023-09-07). 10980:Dey, Nolan (March 28, 2023). 9242:Wang, Yongge (20 June 2024). 8947:Nature Biomedical Engineering 7868:OpenAI (September 25, 2023). 6929:Albrecht, Josh (2024-07-23). 6373:10.18653/v1/2022.acl-long.577 6367:. 1: Long Papers: 8424–8445. 6241:"Pre-trained Language Models" 5392: 5139:Tokyo Institute of Technology 5027:Multimodal model, based on a 3376:Nature Biomedical Engineering 2934: 2929: 1948: 1919:frozen to improve stability. 822:Computational learning theory 386:Expectation–maximization (EM) 12398:Multi-document summarization 11909:"Nemotron-4 340B | Research" 11202:Wiggers, Kyle (2023-04-13). 8498:Roose, Kevin (30 May 2023). 6047:Merritt, Rick (2022-03-25). 4900:the chatbot of the same name 3503:Training cost (petaFLOP-day) 3186:Because language models may 1880:, the post-processed vector 1812:for image-text to text, and 1727:in the subsequent episodes. 1551:Attention (machine learning) 1501:proximal policy optimization 1074:the chatbot of the same name 779:Coefficient of determination 626:Convolutional neural network 338:Support vector machine (SVM) 7: 12885:Natural language processing 12728:Latent Dirichlet allocation 12700:Natural language generation 12565:Machine-readable dictionary 12560:Linguistic Linked Open Data 12135:Natural language processing 11820:"Phi-3 Model Documentation" 7870:"GPT-4V(ision) System Card" 6253:10.1007/978-3-031-23190-2_2 6196:– via openreview.net. 5757:"Attention is All you Need" 5314: 4641:(Pathways Language Model 2) 4246:Large collaboration led by 3801:GPT-3-style language model 3402: 2828:modular arithmetic addition 2426:At point(s) referred to as 2051:They are related by simple 1666: 1419: 988:natural language processing 930:Outline of machine learning 827:Empirical risk minimization 10: 12901: 12480:Explicit semantic analysis 12229:Deep linguistic processing 12074:10.1038/s44159-023-00211-x 11989:, 3rd Edition draft, 2023. 11620:AI, Mistral (2024-04-17). 11530:"Gemini – Google DeepMind" 10670:10.1038/d41586-023-00641-w 9117:Alba, Davey (1 May 2023). 9105:Peng, Wang & Deng 2023 8959:10.1038/s41551-023-01012-6 8953:(2): 85–86. 7 March 2023. 8390:Metz, Cade (16 May 2023). 7665:. Curran Associates, Inc. 5770:. Curran Associates, Inc. 5676:10.1162/089120103322711578 5609:10.1162/089120103322711569 5275:NVIDIA Open Model License 3475: 3438: 3391: 3388:Memorization and copyright 2832:discrete Fourier transform 2786:{\displaystyle (\log x,y)} 2703:{\displaystyle (\log x,y)} 2608:{\displaystyle (\log x,y)} 2462:chain-of-thought prompting 1957: 1789: 1544: 1526: 1492: 1481: 1462: 1443: 1386: 1267: 1143:Neural Machine Translation 1111: 1009:built with a decoder-only 1007:artificial neural networks 567:Feedforward neural network 318:Artificial neural networks 29: 12831: 12786: 12741: 12713: 12673: 12618: 12540: 12528: 12459: 12416: 12388: 12323:Word-sense disambiguation 12199: 12176:Computational linguistics 12141: 12062:Nature Reviews Psychology 9445:"finetune-transformer-lm" 8697:Friston, Karl J. (2022). 7057:Section 2.1 and Table 1, 6645:"Illustrated transformer" 6133:Yennie Jun (2023-05-03). 5663:Computational Linguistics 5597:Computational Linguistics 4104:(Pathways Language Model) 4092:bot. Often cited for its 1995:artificial neural network 1810:visual question answering 1698: 1478:Training and architecture 1166:Attention Is All You Need 1164:in their landmark paper " 550:Artificial neural network 12849:Natural Language Toolkit 12773:Pronunciation assessment 12675:Automatic identification 12505:Latent semantic analysis 12461:Distributional semantics 12346:Compound-term processing 12244:Named-entity recognition 11449:"Introducing Claude 2.1" 10913:"GPT-4 Technical Report" 10340:Table 20 and page 66 of 9249:. IACR ePrint 2024/586. 5715:IEEE Intelligent Systems 5326: 5308:-80GB, at 3.8E25 FLOPs. 4736:Used in Claude chatbot. 4706:1.7 million A100-hours. 4403:Non-commercial research 4308:AlexaTM (Teacher Models) 4168:Non-commercial research 3874:is based on this model. 2180:where the variables are 1968:cost of (pre-)training ( 1860:, so that for any image 1451:training a further LLM. 1250:recurrent neural network 1162:transformer architecture 1044:series of models (e.g., 859:Journals and conferences 806:Mathematical foundations 716:Temporal difference (TD) 572:Recurrent neural network 492:Conditional random field 415:Dimensionality reduction 163:Dimensionality reduction 125:Quantum machine learning 120:Neuromorphic engineering 80:Self-supervised learning 75:Semi-supervised learning 30:Not to be confused with 12753:Automated essay scoring 12723:Document classification 12390:Automatic summarization 11483:, xai-org, 2024-03-19, 11420:"Announcing Mistral 7B" 9367:10.1145/3582269.3615599 9156:10.1126/science.adj2463 8672:Evans, Vyvyan. (2014). 8647:Lakoff, George (1999). 8359:10.1073/pnas.2215907120 7568:Grootendorst, Maarten. 6800:10.1145/3373017.3373028 5644:10.3115/1073012.1073017 5505:Manning, Christopher D. 3471: 2918:mapped out the role of 2319:{\displaystyle C_{0}=6} 2066:schedule, states that: 1911:{\displaystyle f(E(y))} 1731:Monte Carlo tree search 1021:. These models acquire 268:Apprenticeship learning 12610:Universal Dependencies 12303:Terminology extraction 12286:Semantic decomposition 12281:Semantic role labeling 12271:Part-of-speech tagging 12239:Information extraction 12224:Coreference resolution 12214:Collocation extraction 9671:imagen.research.google 9095:Citing Lee et al 2022. 8829:, OpenAI, 2024-05-28, 8163:(Yao Fu, Nov 20, 2022) 4408:Chinchilla scaling law 3870:Chinese-language LLM. 3836:Restricted web access 3360: 3258: 3177: 3157: 3137: 3117: 3097: 3077: 3057: 3004: 2787: 2749: 2704: 2666: 2609: 2571: 2524: 2504: 2431: 2405: 2320: 2274: 2250: 2226: 2198: 2174: 2037: 2012: 1983: 1912: 1874: 1854: 1834: 1649: 1568: 1382: 1183:Although decoder-only 1153: 1130: 1122: 1036:Some notable LLMs are 817:Bias–variance tradeoff 699:Reinforcement learning 675:Spiking neural network 85:Reinforcement learning 32:Logic learning machine 12875:Large language models 12371:Sentence segmentation 11178:www.timesofisrael.com 9776:"GPT-2: 1.5B Release" 9396:MIT Technology Review 8567:ACM Computing Surveys 5945:MIT Technology Review 5916:. November 30, 2023. 3392:Further information: 3348: 3294:commonsense reasoning 3259: 3178: 3158: 3138: 3118: 3098: 3078: 3058: 2984: 2893:cognitive linguistics 2788: 2750: 2705: 2667: 2610: 2572: 2525: 2505: 2425: 2406: 2321: 2275: 2251: 2227: 2199: 2175: 2038: 2013: 1984: 1913: 1875: 1855: 1835: 1648: 1565: 1264:Dataset preprocessing 1151: 1128: 1119: 978:) is a computational 653:Neural radiance field 475:Structured prediction 198:Structured prediction 70:Unsupervised learning 12823:Voice user interface 12534:datasets and corpora 12475:Document-term matrix 12328:Word-sense induction 12043:aiindex.stanford.edu 11983:, Martin, James. H. 11691:. 15 February 2024. 11597:. 11 December 2023. 11591:"Mixtral of experts" 11269:"Introducing PaLM 2" 10855:. 24 February 2023. 10820:. 17 November 2022. 8807:, OpenAI, 2024-05-28 8061:10.18653/v1/N19-1128 7404:voyager.minedojo.org 6723:. December 6, 2023. 6697:. 15 February 2024. 5844:10.1162/tacl_a_00349 5526:10.1162/daed_a_01905 4596:1.5 trillion tokens 3219: 3167: 3147: 3127: 3107: 3087: 3067: 2948: 2895:. American linguist 2759: 2717: 2676: 2622: 2581: 2539: 2514: 2494: 2334: 2297: 2264: 2240: 2216: 2188: 2070: 2027: 2002: 1973: 1884: 1864: 1844: 1824: 1816:for speech to text. 1808:for image to label, 1777:different precisions 1135:IBM alignment models 982:capable of language 972:large language model 842:Statistical learning 740:Learning with humans 532:Local outlier factor 12803:Interactive fiction 12733:Pachinko allocation 12690:Speech segmentation 12646:Google Ngram Viewer 12418:Machine translation 12408:Text simplification 12403:Sentence extraction 12291:Semantic similarity 11913:research.nvidia.com 11795:azure.microsoft.com 11721:– via GitHub. 11505:"Grok-1 model card" 10662:2023Natur.615..202A 10285:. 8 December 2021. 9995:– via GitHub. 9543:– via GitHub. 9284:Scientific American 8804:openai/simple-evals 8351:2023PNAS..12015907M 8335:(13): e2215907120. 7482:. PMLR: 7197–7206. 7455:www.theregister.com 7248:. PMLR: 9118–9147. 6212:platform.openai.com 5977:. November 30, 2023 5727:10.1109/MIS.2009.36 5579:2001cs........8005G 5195:Granite Code Models 4566:329 billion tokens 3806:Megatron-Turing NLG 2922:(PCFG) in enabling 2901:computational basis 2456:cardinal directions 2444:in-context learning 1792:Multimodal learning 1743:curriculum learning 685:Electrochemical RAM 592:reservoir computing 323:Logistic regression 242:Supervised learning 228:Multimodal learning 203:Feature engineering 148:Generative modeling 110:Rule-based learning 105:Curriculum learning 65:Supervised learning 40:Part of a series on 12813:Question answering 12685:Speech recognition 12550:Corpus linguistics 12530:Language resources 12313:Textual entailment 12296:Sentiment analysis 11659:Microsoft Research 11092:2024-02-08 at the 10348:2023-06-10 at the 10131:Microsoft Research 9983:. March 15, 2023. 9531:. March 13, 2023. 8503:The New York Times 8396:The New York Times 8158:2023-06-24 at the 8098:pilehvar.github.io 5975:ourworldindata.org 5058:Gemma Terms of Use 5029:Mixture-of-Experts 4934:Mixture of experts 4477:Chinchilla formula 4094:neural scaling law 3945:mixture of experts 3610:34 billion tokens 3413:Cornell University 3323:-shot prompting). 3254: 3205:information theory 3173: 3153: 3133: 3113: 3093: 3073: 3053: 2881:Terrence Sejnowski 2783: 2745: 2700: 2662: 2605: 2567: 2520: 2500: 2485:smooth scaling law 2432: 2416:Emergent abilities 2401: 2316: 2270: 2246: 2222: 2194: 2170: 2165: 2057:Chinchilla scaling 2033: 2008: 1979: 1960:Neural scaling law 1908: 1870: 1850: 1830: 1814:speech recognition 1689:document retrieval 1650: 1569: 1557:prompt engineering 1547:Prompt engineering 1535:mixture of experts 1529:Mixture of experts 1523:Mixture of experts 1507:Instruction tuning 1389:Byte pair encoding 1367:Tokenization also 1298:control characters 1290:byte-pair encoding 1154: 1131: 1123: 1084:family of models, 1019:prompt engineering 1002:training process. 253: • 168:Density estimation 12862: 12861: 12818:Virtual assistant 12743:Computer-assisted 12669: 12668: 12426:Computer-assisted 12384: 12383: 12376:Word segmentation 12338:Text segmentation 12276:Semantic analysis 12264:Syntactic parsing 12249:Ontology learning 11797:. 23 April 2024. 11737:www.anthropic.com 10769:. 2 August 2022. 10656:(7951): 202–205. 10623:ai.googleblog.com 10487:ai.googleblog.com 10371:ai.googleblog.com 9504:The Next Platform 9422:. June 11, 2018. 9376:979-8-4007-0113-9 8708:978-0-262-36997-8 8701:. The MIT Press. 8683:978-1-107-04396-1 8674:The Language Myth 8658:978-0-465-05674-3 8535:. 13 April 2023. 7892:, timestamp 15:31 7631:. PMLR: 595–603. 7106:reasonwithpal.com 6574:ai.googleblog.com 6218:on April 23, 2023 5321:Foundation models 5312: 5311: 3249: 3225: 3207:, the concept of 3199:BPW, BPC, and BPT 3176:{\displaystyle i} 3156:{\displaystyle i} 3136:{\displaystyle i} 3116:{\displaystyle i} 3096:{\displaystyle i} 3076:{\displaystyle N} 3039: 3038:context for token 3024: 2982: 2963: 2865:stochastic parrot 2740: 2729: 2654: 2634: 2562: 2551: 2523:{\displaystyle y} 2503:{\displaystyle x} 2411: 2326: 2280: 2273:{\displaystyle L} 2256: 2249:{\displaystyle D} 2232: 2225:{\displaystyle N} 2204: 2197:{\displaystyle C} 2148: 2128: 2043: 2036:{\displaystyle D} 2018: 2011:{\displaystyle N} 1989: 1982:{\displaystyle C} 1873:{\displaystyle y} 1853:{\displaystyle f} 1833:{\displaystyle E} 1705:intelligent agent 1395:punctuation marks 1365: 1364: 1318: 1062:Microsoft Copilot 968: 967: 773:Model diagnostics 756:Human-in-the-loop 599:Boltzmann machine 512:Anomaly detection 308:Linear regression 223:Ontology learning 218:Grammar induction 193:Semantic analysis 188:Association rules 173:Anomaly detection 115:Neuro-symbolic AI 16:(Redirected from 12892: 12839:Formal semantics 12788:Natural language 12695:Speech synthesis 12677:and data capture 12580:Semantic network 12555:Lexical resource 12538: 12537: 12356:Lexical analysis 12334: 12333: 12259:Semantic parsing 12128: 12121: 12114: 12105: 12104: 12100: 12098: 12096: 12052: 12050: 12049: 12034: 12032: 12019: 12017: 12004: 12002: 11968: 11967: 11965: 11964: 11945: 11939: 11934: 11928: 11927: 11925: 11924: 11905: 11899: 11898: 11896: 11895: 11876: 11870: 11869: 11867: 11866: 11845: 11839: 11838: 11836: 11835: 11816: 11810: 11809: 11807: 11806: 11787: 11781: 11780: 11778: 11777: 11758: 11752: 11751: 11749: 11748: 11729: 11723: 11722: 11715: 11709: 11708: 11702: 11700: 11681: 11675: 11674: 11672: 11670: 11650: 11641: 11640: 11638: 11637: 11617: 11611: 11610: 11608: 11606: 11587: 11581: 11580: 11578: 11576: 11556: 11550: 11549: 11547: 11545: 11526: 11520: 11519: 11517: 11515: 11501: 11495: 11494: 11493: 11492: 11475: 11469: 11468: 11466: 11464: 11445: 11439: 11438: 11436: 11435: 11416: 11410: 11409: 11407: 11406: 11386: 11380: 11379: 11377: 11375: 11356: 11350: 11349: 11347: 11346: 11327: 11321: 11320: 11318: 11317: 11298: 11289: 11288: 11286: 11284: 11275:. May 10, 2023. 11265: 11259: 11258: 11256: 11254: 11232: 11223: 11222: 11220: 11219: 11199: 11193: 11192: 11190: 11189: 11172:Wrobel, Sharon. 11169: 11163: 11162: 11160: 11147: 11141: 11140: 11138: 11125: 11119: 11118: 11116: 11103: 11097: 11084: 11078: 11077: 11075: 11074: 11059: 11053: 11052: 11050: 11037: 11031: 11030: 11028: 11027: 11008: 11002: 11001: 10999: 10997: 10977: 10971: 10970: 10968: 10967: 10947: 10941: 10940: 10938: 10936: 10930: 10917: 10909: 10903: 10902: 10900: 10899: 10880: 10869: 10868: 10866: 10864: 10845: 10834: 10833: 10831: 10829: 10810: 10804: 10803: 10801: 10789: 10783: 10782: 10780: 10778: 10759: 10753: 10752: 10750: 10737: 10731: 10730: 10728: 10727: 10708: 10702: 10701: 10699: 10697: 10641: 10635: 10634: 10632: 10630: 10615: 10609: 10608: 10606: 10593: 10584: 10583: 10582: 10581: 10563: 10554: 10553: 10551: 10538: 10532: 10531: 10529: 10528: 10508: 10502: 10501: 10499: 10498: 10478: 10472: 10471: 10469: 10467: 10447: 10436: 10435: 10433: 10432: 10414: 10408: 10407: 10405: 10392: 10386: 10385: 10383: 10382: 10362: 10353: 10338: 10325: 10324: 10322: 10310: 10299: 10298: 10296: 10294: 10283:www.deepmind.com 10275: 10269: 10268: 10266: 10254: 10248: 10247: 10245: 10233: 10224: 10223: 10221: 10219: 10200: 10194: 10193: 10191: 10178: 10172: 10171: 10169: 10156: 10147: 10146: 10144: 10142: 10122: 10116: 10115: 10113: 10100: 10087: 10086: 10084: 10083: 10074:. 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12895: 12894: 12893: 12891: 12890: 12889: 12865: 12864: 12863: 12858: 12827: 12807:Syntax guessing 12789: 12782: 12768:Predictive text 12763:Grammar checker 12744: 12737: 12709: 12676: 12665: 12631:Bank of English 12614: 12542: 12533: 12524: 12455: 12412: 12380: 12332: 12234:Distant reading 12209:Argument mining 12195: 12191:Text processing 12137: 12132: 12094: 12092: 12047: 12045: 12037: 11977: 11975:Further reading 11972: 11971: 11962: 11960: 11947: 11946: 11942: 11935: 11931: 11922: 11920: 11907: 11906: 11902: 11893: 11891: 11878: 11877: 11873: 11864: 11862: 11847: 11846: 11842: 11833: 11831: 11818: 11817: 11813: 11804: 11802: 11789: 11788: 11784: 11775: 11773: 11760: 11759: 11755: 11746: 11744: 11731: 11730: 11726: 11717: 11716: 11712: 11698: 11696: 11683: 11682: 11678: 11668: 11666: 11651: 11644: 11635: 11633: 11618: 11614: 11604: 11602: 11589: 11588: 11584: 11574: 11572: 11557: 11553: 11543: 11541: 11534:deepmind.google 11528: 11527: 11523: 11513: 11511: 11503: 11502: 11498: 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4265:Responsible AI 4260:tokens (1.6TB) 3844:Ernie 3.0 Titan 3480: 3474: 3465: 3452: 3443: 3437: 3405: 3396: 3390: 3371: 3357: 3355: 3353: 3351: 3329: 3286: 3246: 3234: 3230: 3222: 3220: 3217: 3216: 3201: 3168: 3165: 3164: 3148: 3145: 3144: 3128: 3125: 3124: 3108: 3105: 3104: 3088: 3085: 3084: 3068: 3065: 3064: 3041: 3036: 3035: 3026: 3021: 3020: 2999: 2988: 2974: 2960: 2949: 2946: 2945: 2937: 2932: 2840: 2801: 2796: 2760: 2757: 2756: 2737: 2726: 2718: 2715: 2714: 2677: 2674: 2673: 2651: 2631: 2623: 2620: 2619: 2582: 2579: 2578: 2559: 2548: 2540: 2537: 2536: 2515: 2512: 2511: 2495: 2492: 2491: 2418: 2389: 2385: 2335: 2332: 2331: 2304: 2300: 2298: 2295: 2294: 2265: 2262: 2261: 2241: 2238: 2237: 2217: 2214: 2213: 2189: 2186: 2185: 2164: 2163: 2157: 2153: 2142: 2138: 2133: 2122: 2118: 2113: 2104: 2103: 2091: 2087: 2074: 2073: 2071: 2068: 2067: 2028: 2025: 2024: 2003: 2000: 1999: 1974: 1971: 1970: 1962: 1956: 1951: 1939:Google DeepMind 1885: 1882: 1881: 1865: 1862: 1861: 1845: 1842: 1841: 1825: 1822: 1821: 1794: 1788: 1758: 1701: 1693:vector database 1669: 1643: 1635: 1614: 1553: 1543: 1531: 1525: 1509: 1497: 1491: 1486: 1480: 1467: 1461: 1448: 1442: 1422: 1391: 1385: 1315: 1308: 1301: 1277: 1272: 1266: 1114: 1000:semi-supervised 996:self-supervised 992:language models 964: 935: 934: 908: 900: 899: 860: 852: 851: 812:Kernel machines 807: 799: 798: 774: 766: 765: 746:Active learning 741: 733: 732: 701: 691: 690: 616:Diffusion model 552: 542: 541: 514: 504: 503: 477: 467: 466: 422:Factor analysis 417: 407: 406: 390: 353: 343: 342: 263: 262: 246: 245: 244: 233: 232: 138: 130: 129: 95:Online learning 60: 48: 35: 28: 23: 22: 15: 12: 11: 5: 12898: 12888: 12887: 12882: 12877: 12860: 12859: 12857: 12856: 12851: 12846: 12841: 12835: 12833: 12829: 12828: 12826: 12825: 12820: 12815: 12810: 12800: 12794: 12792: 12790:user interface 12784: 12783: 12781: 12780: 12775: 12770: 12765: 12760: 12755: 12749: 12747: 12739: 12738: 12736: 12735: 12730: 12725: 12719: 12717: 12711: 12710: 12708: 12707: 12702: 12697: 12692: 12687: 12681: 12679: 12671: 12670: 12667: 12666: 12664: 12663: 12658: 12653: 12648: 12643: 12638: 12633: 12628: 12622: 12620: 12616: 12615: 12613: 12612: 12607: 12602: 12597: 12592: 12587: 12582: 12577: 12572: 12567: 12562: 12557: 12552: 12546: 12544: 12535: 12526: 12525: 12523: 12522: 12517: 12515:Word embedding 12512: 12507: 12502: 12495:Language model 12492: 12487: 12482: 12477: 12472: 12466: 12464: 12457: 12456: 12454: 12453: 12448: 12446:Transfer-based 12443: 12438: 12433: 12428: 12422: 12420: 12414: 12413: 12411: 12410: 12405: 12400: 12394: 12392: 12386: 12385: 12382: 12381: 12379: 12378: 12373: 12368: 12363: 12358: 12353: 12348: 12342: 12340: 12331: 12330: 12325: 12320: 12315: 12310: 12305: 12299: 12298: 12293: 12288: 12283: 12278: 12273: 12268: 12267: 12266: 12261: 12251: 12246: 12241: 12236: 12231: 12226: 12221: 12219:Concept mining 12216: 12211: 12205: 12203: 12197: 12196: 12194: 12193: 12188: 12183: 12178: 12173: 12172: 12171: 12166: 12156: 12151: 12145: 12143: 12139: 12138: 12131: 12130: 12123: 12116: 12108: 12102: 12101: 12068:(8): 451–452. 12053: 12035: 12020: 12005: 11990: 11976: 11973: 11970: 11969: 11940: 11929: 11900: 11886:. 2024-06-14. 11884:huggingface.co 11871: 11840: 11824:huggingface.co 11811: 11782: 11766:huggingface.co 11753: 11724: 11710: 11676: 11642: 11612: 11582: 11551: 11521: 11496: 11480:xai-org/grok-1 11470: 11440: 11411: 11381: 11351: 11322: 11290: 11260: 11224: 11194: 11164: 11142: 11120: 11098: 11079: 11067:huggingface.co 11054: 11032: 11003: 10972: 10942: 10904: 10888:huggingface.co 10870: 10835: 10818:aws.amazon.com 10805: 10784: 10767:Amazon Science 10754: 10732: 10716:huggingface.co 10703: 10636: 10625:. 30 June 2022 10610: 10585: 10555: 10533: 10503: 10473: 10437: 10409: 10387: 10354: 10326: 10300: 10270: 10249: 10225: 10195: 10173: 10148: 10117: 10088: 10059: 10025: 9998: 9972: 9958:. 2022-11-30. 9943: 9920: 9890: 9874:huggingface.co 9858: 9841:lambdalabs.com 9825: 9796: 9782:. 2019-11-05. 9767: 9746: 9716: 9700:huggingface.co 9687: 9658: 9632: 9588: 9567: 9546: 9520: 9490: 9466: 9436: 9407: 9382: 9375: 9345: 9321: 9300: 9267: 9234: 9213: 9192: 9173: 9135: 9109: 9097: 9040: 9010: 8995:. 7 May 2023. 8980: 8937: 8913: 8892: 8871: 8842: 8816: 8794: 8767: 8743: 8714: 8707: 8689: 8682: 8664: 8657: 8639: 8618: 8550: 8520: 8490: 8473:The New Yorker 8456: 8426: 8401: 8382: 8306: 8285: 8264: 8239: 8217: 8196: 8165: 8144: 8114: 8085: 8039: 8018: 7997: 7973: 7949: 7927: 7901: 7878: 7860: 7839: 7818: 7797: 7775: 7730: 7709: 7679: 7645: 7615: 7590: 7580:on 31 Jul 2024 7560: 7538: 7517: 7496: 7466: 7449:Mann, Tobias. 7441: 7420: 7388: 7367: 7346: 7325: 7303: 7282: 7261: 7232: 7187: 7166: 7144: 7122: 7093: 7072: 7047: 7023: 7001: 6980: 6946: 6921: 6892: 6863: 6823: 6808: 6768: 6738: 6712: 6682: 6669:Allamar, Jay. 6661: 6643:Allamar, Jay. 6635: 6590: 6553: 6531: 6509: 6488: 6466: 6444: 6423: 6402: 6378: 6348: 6326: 6301: 6280: 6261: 6228: 6199: 6158: 6125: 6093: 6069: 6039: 6018: 6001:huggingface.co 5988: 5962: 5931: 5901: 5868: 5808: 5787: 5753:Gomez, Aidan N 5740: 5701: 5669:(3): 349–380. 5649: 5622: 5603:(3): 333–347. 5583: 5551: 5519:(2): 127–138. 5493: 5473: 5427: 5413:. 2019-02-14. 5397: 5396: 5394: 5391: 5388: 5387: 5377: 5368: 5359: 5350: 5341: 5331: 5330: 5328: 5325: 5324: 5323: 5316: 5313: 5310: 5309: 5302: 5299: 5296: 5293: 5290: 5287: 5284: 5280: 5279: 5276: 5273: 5270: 5267: 5262: 5257: 5254: 5250: 5249: 5246: 5244: 5242: 5239: 5236: 5231: 5226: 5222: 5221: 5219: 5216: 5213: 5210: 5207: 5202: 5197: 5191: 5190: 5187: 5184: 5182: 5179: 5176: 5173: 5168: 5162: 5161: 5154: 5152: 5150: 5147: 5142: 5132: 5129: 5125: 5124: 5121: 5118: 5116: 5113: 5108: 5099: 5096: 5090: 5089: 5086: 5083: 5080: 5077: 5074: 5071: 5068: 5062: 5061: 5059: 5056: 5053: 5050: 5047: 5042: 5037: 5033: 5032: 5025: 5022: 5019: 5016: 5013: 5008: 5003: 4997: 4996: 4993: 4990: 4987: 4984: 4981: 4978: 4973: 4967: 4966: 4964: 4961: 4958: 4955: 4952: 4947: 4942: 4941:Mixtral 8x22B 4938: 4937: 4930: 4927: 4924: 4921: 4918: 4913: 4908: 4904: 4903: 4896: 4893: 4890: 4887: 4884: 4879: 4874: 4868: 4867: 4860: 4857: 4854: 4851: 4848: 4843: 4838: 4834: 4833: 4830: 4827: 4824: 4821: 4818: 4815: 4810: 4804: 4803: 4801: 4798: 4796: 4793: 4788: 4783: 4781:September 2023 4778: 4774: 4773: 4766: 4763: 4760: 4757: 4754: 4749: 4744: 4738: 4737: 4734: 4731: 4728: 4725: 4722: 4719: 4714: 4708: 4707: 4704: 4701: 4698: 4692: 4687: 4684: 4679: 4675: 4674: 4667: 4664: 4661: 4655: 4650: 4647: 4642: 4635: 4634: 4631: 4628: 4626: 4623: 4620: 4615: 4610: 4606: 4605: 4602: 4599: 4597: 4594: 4589: 4584: 4579: 4575: 4574: 4572: 4569: 4567: 4564: 4559: 4554: 4549: 4543: 4542: 4539: 4536: 4534: 4531: 4526: 4524:Bloomberg L.P. 4521: 4516: 4512: 4511: 4509: 4506: 4503: 4500: 4495: 4490: 4485: 4481: 4480: 4473: 4470: 4467: 4465: 4460: 4455: 4450: 4446: 4445: 4438: 4435: 4432: 4429: 4426: 4423: 4418: 4412: 4411: 4404: 4401: 4398: 4393: 4388: 4383: 4378: 4371: 4370: 4363: 4360: 4358: 4355: 4352: 4349: 4344: 4338: 4337: 4334: 4331: 4329: 4324: 4319: 4314: 4309: 4305: 4304: 4301: 4298: 4295: 4289: 4284: 4279: 4274: 4270: 4269: 4266: 4263: 4261: 4255: 4250: 4244: 4239: 4233: 4232: 4229: 4226: 4224: 4221: 4216: 4213: 4208: 4204: 4203: 4200: 4197: 4195: 4192: 4187: 4182: 4177: 4173: 4172: 4169: 4166: 4163: 4157: 4152: 4147: 4142: 4138: 4137: 4130: 4127: 4124: 4118: 4113: 4110: 4105: 4098: 4097: 4086: 4083: 4080: 4074: 4069: 4064: 4059: 4053: 4052: 4049: 4046: 4043: 4040: 4035: 4030: 4025: 4021: 4020: 4017: 4014: 4011: 4004: 3999: 3996: 3991: 3984: 3983: 3980: 3977: 3974: 3968: 3963: 3958: 3953: 3949: 3948: 3941: 3938: 3935: 3929: 3924: 3921: 3916: 3912: 3911: 3908: 3905: 3903: 3897: 3892: 3887: 3882: 3876: 3875: 3868: 3865: 3863: 3860: 3855: 3850: 3845: 3841: 3840: 3837: 3834: 3832: 3826: 3821: 3812: 3807: 3803: 3802: 3799: 3796: 3793: 3790: 3785: 3780: 3775: 3769: 3768: 3761: 3758: 3756: 3753: 3748: 3743: 3738: 3734: 3733: 3726: 3723: 3720: 3714: 3709: 3706: 3701: 3695: 3694: 3691: 3688: 3685: 3678: 3673: 3668: 3663: 3657: 3656: 3653: 3650: 3647: 3646:billion words 3641: 3636: 3631: 3626: 3620: 3619: 3616: 3613: 3611: 3608: 3605: 3602: 3597: 3591: 3590: 3583: 3580: 3575: 3569: 3564: 3559: 3554: 3548: 3547: 3540: 3537: 3534: 3532: 3527: 3522: 3517: 3511: 3510: 3507: 3504: 3501: 3498: 3495: 3492: 3489: 3473: 3470: 3464: 3463:Political bias 3461: 3451: 3448: 3439:Main article: 3436: 3433: 3404: 3401: 3389: 3386: 3370: 3367: 3328: 3325: 3285: 3282: 3253: 3245: 3242: 3237: 3233: 3229: 3213:Claude Shannon 3200: 3197: 3172: 3152: 3132: 3112: 3092: 3072: 3052: 3049: 3044: 3034: 3029: 3019: 3016: 3013: 3010: 3007: 3002: 2997: 2994: 2991: 2987: 2981: 2978: 2973: 2970: 2967: 2959: 2956: 2953: 2936: 2933: 2931: 2928: 2839: 2836: 2821:Karel programs 2800: 2799:Interpretation 2797: 2795: 2794: 2782: 2779: 2776: 2773: 2770: 2767: 2764: 2744: 2736: 2733: 2725: 2722: 2711: 2699: 2696: 2693: 2690: 2687: 2684: 2681: 2661: 2658: 2650: 2647: 2644: 2641: 2638: 2630: 2627: 2616: 2604: 2601: 2598: 2595: 2592: 2589: 2586: 2566: 2558: 2555: 2547: 2544: 2532: 2519: 2499: 2477: 2476: 2465: 2459: 2417: 2414: 2413: 2412: 2400: 2397: 2392: 2388: 2384: 2381: 2378: 2375: 2372: 2369: 2366: 2363: 2360: 2357: 2354: 2351: 2348: 2345: 2342: 2339: 2328: 2315: 2312: 2307: 2303: 2287: 2286: 2269: 2258: 2245: 2234: 2221: 2210: 2193: 2167: 2160: 2156: 2152: 2145: 2141: 2137: 2132: 2125: 2121: 2117: 2112: 2109: 2106: 2105: 2102: 2099: 2094: 2090: 2086: 2083: 2080: 2079: 2077: 2049: 2048: 2045: 2032: 2020: 2007: 1991: 1978: 1958:Main article: 1955: 1952: 1950: 1947: 1907: 1904: 1901: 1898: 1895: 1892: 1889: 1869: 1849: 1829: 1802:proprioception 1787: 1784: 1765:Post-training 1757: 1754: 1700: 1697: 1668: 1665: 1642: 1639: 1634: 1633:Infrastructure 1631: 1623:regularization 1618: 1617: 1607: 1542: 1539: 1527:Main article: 1524: 1521: 1508: 1505: 1493:Main article: 1490: 1487: 1479: 1476: 1465:Synthetic data 1463:Main article: 1460: 1459:Synthetic data 1457: 1446:Data cleansing 1444:Main article: 1441: 1438: 1421: 1418: 1387:Main article: 1384: 1381: 1363: 1362: 1359: 1356: 1353: 1350: 1347: 1344: 1341: 1338: 1335: 1332: 1329: 1326: 1276: 1273: 1265: 1262: 1243:Apache License 1113: 1110: 966: 965: 963: 962: 955: 948: 940: 937: 936: 933: 932: 927: 926: 925: 915: 909: 906: 905: 902: 901: 898: 897: 892: 887: 882: 877: 872: 867: 861: 858: 857: 854: 853: 850: 849: 844: 839: 834: 832:Occam learning 829: 824: 819: 814: 808: 805: 804: 801: 800: 797: 796: 791: 789:Learning curve 786: 781: 775: 772: 771: 768: 767: 764: 763: 758: 753: 748: 742: 739: 738: 735: 734: 731: 730: 729: 728: 718: 713: 708: 702: 697: 696: 693: 692: 689: 688: 682: 677: 672: 667: 666: 665: 655: 650: 649: 648: 643: 638: 633: 623: 618: 613: 608: 607: 606: 596: 595: 594: 589: 584: 579: 569: 564: 559: 553: 548: 547: 544: 543: 540: 539: 534: 529: 521: 515: 510: 509: 506: 505: 502: 501: 500: 499: 494: 489: 478: 473: 472: 469: 468: 465: 464: 459: 454: 449: 444: 439: 434: 429: 424: 418: 413: 412: 409: 408: 405: 404: 399: 394: 388: 383: 378: 370: 365: 360: 354: 349: 348: 345: 344: 341: 340: 335: 330: 325: 320: 315: 310: 305: 297: 296: 295: 290: 285: 275: 273:Decision trees 270: 264: 250:classification 240: 239: 238: 235: 234: 231: 230: 225: 220: 215: 210: 205: 200: 195: 190: 185: 180: 175: 170: 165: 160: 155: 150: 145: 143:Classification 139: 136: 135: 132: 131: 128: 127: 122: 117: 112: 107: 102: 100:Batch learning 97: 92: 87: 82: 77: 72: 67: 61: 58: 57: 54: 53: 42: 41: 26: 9: 6: 4: 3: 2: 12897: 12886: 12883: 12881: 12880:Deep learning 12878: 12876: 12873: 12872: 12870: 12855: 12852: 12850: 12847: 12845: 12844:Hallucination 12842: 12840: 12837: 12836: 12834: 12830: 12824: 12821: 12819: 12816: 12814: 12811: 12808: 12804: 12801: 12799: 12796: 12795: 12793: 12791: 12785: 12779: 12778:Spell checker 12776: 12774: 12771: 12769: 12766: 12764: 12761: 12759: 12756: 12754: 12751: 12750: 12748: 12746: 12740: 12734: 12731: 12729: 12726: 12724: 12721: 12720: 12718: 12716: 12712: 12706: 12703: 12701: 12698: 12696: 12693: 12691: 12688: 12686: 12683: 12682: 12680: 12678: 12672: 12662: 12659: 12657: 12654: 12652: 12649: 12647: 12644: 12642: 12639: 12637: 12634: 12632: 12629: 12627: 12624: 12623: 12621: 12617: 12611: 12608: 12606: 12603: 12601: 12598: 12596: 12593: 12591: 12590:Speech corpus 12588: 12586: 12583: 12581: 12578: 12576: 12573: 12571: 12570:Parallel text 12568: 12566: 12563: 12561: 12558: 12556: 12553: 12551: 12548: 12547: 12545: 12539: 12536: 12531: 12527: 12521: 12518: 12516: 12513: 12511: 12508: 12506: 12503: 12500: 12496: 12493: 12491: 12488: 12486: 12483: 12481: 12478: 12476: 12473: 12471: 12468: 12467: 12465: 12462: 12458: 12452: 12449: 12447: 12444: 12442: 12439: 12437: 12434: 12432: 12431:Example-based 12429: 12427: 12424: 12423: 12421: 12419: 12415: 12409: 12406: 12404: 12401: 12399: 12396: 12395: 12393: 12391: 12387: 12377: 12374: 12372: 12369: 12367: 12364: 12362: 12361:Text chunking 12359: 12357: 12354: 12352: 12351:Lemmatisation 12349: 12347: 12344: 12343: 12341: 12339: 12335: 12329: 12326: 12324: 12321: 12319: 12316: 12314: 12311: 12309: 12306: 12304: 12301: 12300: 12297: 12294: 12292: 12289: 12287: 12284: 12282: 12279: 12277: 12274: 12272: 12269: 12265: 12262: 12260: 12257: 12256: 12255: 12252: 12250: 12247: 12245: 12242: 12240: 12237: 12235: 12232: 12230: 12227: 12225: 12222: 12220: 12217: 12215: 12212: 12210: 12207: 12206: 12204: 12202: 12201:Text analysis 12198: 12192: 12189: 12187: 12184: 12182: 12179: 12177: 12174: 12170: 12167: 12165: 12162: 12161: 12160: 12157: 12155: 12152: 12150: 12147: 12146: 12144: 12142:General terms 12140: 12136: 12129: 12124: 12122: 12117: 12115: 12110: 12109: 12106: 12091: 12087: 12083: 12079: 12075: 12071: 12067: 12063: 12059: 12054: 12044: 12040: 12036: 12031: 12026: 12021: 12016: 12011: 12006: 12001: 11996: 11991: 11988: 11987: 11982: 11981:Jurafsky, Dan 11979: 11978: 11958: 11954: 11950: 11944: 11938: 11933: 11918: 11914: 11910: 11904: 11889: 11885: 11881: 11875: 11860: 11856: 11855: 11850: 11844: 11829: 11825: 11821: 11815: 11800: 11796: 11792: 11786: 11771: 11767: 11763: 11757: 11742: 11738: 11734: 11728: 11720: 11714: 11707: 11694: 11690: 11686: 11680: 11664: 11660: 11656: 11649: 11647: 11631: 11627: 11623: 11616: 11600: 11596: 11592: 11586: 11570: 11566: 11562: 11555: 11539: 11535: 11531: 11525: 11510: 11506: 11500: 11486: 11482: 11481: 11474: 11458: 11454: 11453:anthropic.com 11450: 11444: 11429: 11425: 11421: 11415: 11400: 11396: 11392: 11385: 11369: 11365: 11364:anthropic.com 11361: 11355: 11340: 11336: 11332: 11326: 11311: 11307: 11303: 11297: 11295: 11278: 11274: 11270: 11264: 11248: 11244: 11243: 11238: 11231: 11229: 11213: 11209: 11205: 11198: 11183: 11179: 11175: 11168: 11159: 11154: 11146: 11137: 11132: 11124: 11115: 11110: 11102: 11096:, 31 May 2023 11095: 11091: 11088: 11083: 11068: 11064: 11058: 11049: 11044: 11036: 11021: 11017: 11013: 11007: 10991: 10987: 10983: 10976: 10961: 10957: 10953: 10946: 10927: 10923: 10922: 10914: 10908: 10893: 10889: 10885: 10879: 10877: 10875: 10858: 10854: 10850: 10844: 10842: 10840: 10823: 10819: 10815: 10809: 10800: 10795: 10788: 10772: 10768: 10764: 10758: 10749: 10744: 10736: 10721: 10717: 10713: 10707: 10691: 10687: 10683: 10679: 10675: 10671: 10667: 10663: 10659: 10655: 10651: 10647: 10640: 10624: 10620: 10614: 10605: 10600: 10592: 10590: 10575: 10571: 10570: 10562: 10560: 10550: 10545: 10537: 10522: 10518: 10514: 10507: 10492: 10488: 10484: 10477: 10461: 10457: 10456:Deepmind Blog 10453: 10446: 10444: 10442: 10426: 10422: 10421: 10413: 10404: 10399: 10391: 10376: 10372: 10368: 10361: 10359: 10352: 10351: 10347: 10344: 10337: 10335: 10333: 10331: 10321: 10316: 10309: 10307: 10305: 10288: 10284: 10280: 10274: 10265: 10260: 10253: 10244: 10239: 10232: 10230: 10213: 10209: 10205: 10199: 10190: 10185: 10177: 10168: 10163: 10155: 10153: 10136: 10132: 10128: 10121: 10112: 10107: 10099: 10097: 10095: 10093: 10078:on 2023-03-09 10077: 10073: 10069: 10063: 10047: 10043: 10039: 10032: 10030: 10020: 10015: 10007: 10005: 10003: 9986: 9982: 9976: 9961: 9957: 9953: 9947: 9938: 9933: 9927:Table D.1 in 9924: 9908: 9904: 9900: 9894: 9879: 9875: 9871: 9865: 9863: 9846: 9842: 9838: 9832: 9830: 9814: 9810: 9806: 9800: 9785: 9781: 9777: 9771: 9762: 9757: 9750: 9734: 9730: 9726: 9720: 9705: 9701: 9697: 9691: 9676: 9672: 9668: 9662: 9648: 9644: 9643: 9636: 9628: 9624: 9619: 9614: 9611:(140): 1–67. 9610: 9606: 9602: 9595: 9593: 9583: 9578: 9571: 9562: 9557: 9550: 9534: 9530: 9524: 9509: 9505: 9501: 9494: 9485: 9480: 9473: 9471: 9454: 9450: 9446: 9440: 9425: 9421: 9417: 9411: 9397: 9393: 9386: 9378: 9372: 9368: 9364: 9360: 9356: 9349: 9341: 9336: 9332: 9325: 9316: 9311: 9304: 9289: 9285: 9281: 9274: 9272: 9252: 9245: 9238: 9229: 9224: 9217: 9208: 9203: 9196: 9188: 9184: 9177: 9161: 9157: 9153: 9149: 9145: 9139: 9124: 9120: 9113: 9106: 9101: 9082: 9078: 9074: 9070: 9066: 9062: 9058: 9051: 9044: 9028: 9024: 9023:Goldman Sachs 9020: 9014: 8998: 8994: 8993:The Economist 8990: 8984: 8976: 8972: 8968: 8964: 8960: 8956: 8952: 8948: 8941: 8932: 8927: 8920: 8918: 8908: 8903: 8896: 8887: 8882: 8875: 8860: 8856: 8852: 8846: 8832: 8828: 8827: 8820: 8806: 8805: 8798: 8789: 8784: 8776: 8774: 8772: 8762: 8757: 8750: 8748: 8732: 8728: 8721: 8719: 8710: 8704: 8700: 8693: 8685: 8679: 8675: 8668: 8660: 8654: 8650: 8643: 8634: 8629: 8622: 8606: 8602: 8598: 8594: 8590: 8585: 8580: 8576: 8572: 8568: 8561: 8554: 8538: 8534: 8533:Time Magazine 8530: 8524: 8508: 8504: 8501: 8494: 8478: 8474: 8470: 8463: 8461: 8444: 8440: 8436: 8430: 8421: 8416: 8408: 8406: 8397: 8393: 8386: 8378: 8374: 8369: 8364: 8360: 8356: 8352: 8348: 8343: 8338: 8334: 8330: 8326: 8319: 8317: 8315: 8313: 8311: 8301: 8296: 8289: 8280: 8275: 8268: 8253: 8249: 8243: 8234: 8229: 8221: 8212: 8207: 8200: 8184: 8180: 8176: 8169: 8162: 8161: 8157: 8154: 8148: 8133: 8129: 8125: 8118: 8103: 8099: 8095: 8089: 8074: 8070: 8066: 8062: 8058: 8054: 8050: 8043: 8034: 8029: 8022: 8013: 8008: 8001: 7987: 7983: 7977: 7968: 7963: 7956: 7954: 7944: 7939: 7931: 7916: 7912: 7905: 7891: 7890: 7882: 7871: 7864: 7855: 7850: 7843: 7834: 7829: 7822: 7813: 7808: 7801: 7792: 7787: 7779: 7764: 7759: 7754: 7750: 7746: 7742: 7734: 7725: 7720: 7713: 7698: 7695:: 2425–2433. 7694: 7690: 7683: 7668: 7664: 7660: 7656: 7649: 7634: 7630: 7626: 7619: 7610: 7605: 7601: 7600:Holtzman, Ari 7594: 7579: 7575: 7571: 7564: 7555: 7550: 7542: 7533: 7528: 7521: 7512: 7507: 7500: 7485: 7481: 7477: 7470: 7456: 7452: 7445: 7436: 7431: 7424: 7409: 7405: 7401: 7395: 7393: 7383: 7378: 7371: 7362: 7357: 7350: 7341: 7336: 7329: 7320: 7315: 7307: 7298: 7293: 7286: 7277: 7272: 7265: 7256: 7251: 7247: 7243: 7236: 7221: 7216: 7211: 7207: 7203: 7199: 7191: 7182: 7177: 7170: 7161: 7156: 7148: 7139: 7134: 7126: 7111: 7107: 7103: 7097: 7088: 7083: 7076: 7067: 7062: 7054: 7052: 7043: 7038: 7034: 7027: 7018: 7013: 7005: 6996: 6991: 6984: 6968: 6964: 6960: 6953: 6951: 6936: 6932: 6925: 6910: 6906: 6902: 6896: 6881: 6877: 6873: 6867: 6848: 6841: 6840: 6832: 6830: 6828: 6819: 6815: 6811: 6809:9781450376976 6805: 6801: 6797: 6792: 6787: 6783: 6779: 6772: 6756: 6752: 6748: 6747:"Rate limits" 6742: 6726: 6722: 6716: 6700: 6696: 6692: 6686: 6672: 6665: 6650: 6646: 6639: 6623: 6619: 6615: 6611: 6607: 6599: 6597: 6595: 6579: 6575: 6571: 6564: 6562: 6560: 6558: 6548: 6543: 6535: 6526: 6521: 6513: 6504: 6499: 6492: 6483: 6478: 6470: 6461: 6456: 6448: 6439: 6434: 6427: 6418: 6413: 6406: 6398: 6393: 6389: 6382: 6374: 6370: 6366: 6359: 6352: 6343: 6338: 6330: 6316: 6312: 6305: 6296: 6291: 6284: 6268: 6264: 6262:9783031231902 6258: 6254: 6250: 6246: 6242: 6235: 6233: 6217: 6213: 6209: 6203: 6192:September 16, 6187: 6182: 6177: 6173: 6169: 6162: 6155: 6145:on 2023-08-17 6144: 6140: 6136: 6129: 6115: 6110: 6106: 6105: 6097: 6089: 6084: 6080: 6073: 6058: 6054: 6050: 6043: 6034: 6029: 6022: 6006: 6002: 5998: 5992: 5976: 5972: 5966: 5950: 5946: 5942: 5935: 5919: 5915: 5911: 5905: 5889: 5885: 5884: 5879: 5872: 5857: 5853: 5849: 5845: 5841: 5836: 5831: 5827: 5823: 5819: 5812: 5803: 5798: 5791: 5773: 5769: 5765: 5758: 5754: 5750: 5744: 5736: 5732: 5728: 5724: 5720: 5716: 5712: 5705: 5690: 5686: 5682: 5677: 5672: 5668: 5664: 5660: 5653: 5645: 5641: 5637: 5633: 5626: 5618: 5614: 5610: 5606: 5602: 5598: 5594: 5587: 5580: 5576: 5571: 5566: 5562: 5555: 5540: 5536: 5532: 5527: 5522: 5518: 5514: 5510: 5506: 5500: 5498: 5486: 5485: 5477: 5459: 5455: 5451: 5444: 5436: 5434: 5432: 5416: 5412: 5408: 5402: 5398: 5381: 5372: 5363: 5354: 5345: 5336: 5332: 5322: 5319: 5318: 5307: 5303: 5300: 5297: 5295:15.6T tokens 5294: 5291: 5288: 5285: 5282: 5281: 5277: 5274: 5271: 5268: 5263: 5261: 5258: 5255: 5252: 5251: 5247: 5245: 5243: 5240: 5237: 5235: 5234:Alibaba Cloud 5232: 5227: 5224: 5223: 5220: 5217: 5214: 5211: 5208: 5206: 5203: 5198: 5196: 5193: 5192: 5188: 5185: 5183: 5180: 5177: 5174: 5169: 5167: 5164: 5163: 5159: 5155: 5153: 5151: 5148: 5143: 5140: 5136: 5133: 5130: 5127: 5126: 5122: 5119: 5117: 5114: 5109: 5107: 5103: 5100: 5097: 5095: 5092: 5091: 5087: 5084: 5081: 5078: 5075: 5072: 5069: 5067: 5064: 5063: 5060: 5057: 5054: 5051: 5048: 5046: 5043: 5040:February 2024 5038: 5035: 5034: 5030: 5026: 5023: 5020: 5017: 5014: 5012: 5009: 5006:February 2024 5004: 5002: 4999: 4998: 4994: 4991: 4988: 4985: 4982: 4979: 4976:December 2023 4974: 4972: 4969: 4968: 4965: 4962: 4959: 4956: 4953: 4951: 4948: 4943: 4940: 4939: 4935: 4931: 4928: 4925: 4922: 4919: 4917: 4914: 4911:December 2023 4909: 4907:Mixtral 8x7B 4906: 4905: 4901: 4897: 4894: 4891: 4888: 4885: 4883: 4880: 4877:December 2023 4875: 4873: 4870: 4869: 4865: 4861: 4858: 4855: 4852: 4849: 4847: 4844: 4841:November 2023 4839: 4836: 4835: 4831: 4828: 4825: 4822: 4819: 4816: 4813:November 2023 4811: 4809: 4806: 4805: 4802: 4799: 4797: 4794: 4789: 4787: 4784: 4779: 4776: 4775: 4771: 4767: 4764: 4761: 4758: 4755: 4753: 4750: 4745: 4743: 4740: 4739: 4735: 4732: 4729: 4726: 4723: 4720: 4715: 4713: 4710: 4709: 4705: 4702: 4699: 4693: 4688: 4685: 4680: 4677: 4676: 4672: 4668: 4665: 4662: 4656: 4651: 4648: 4643: 4640: 4637: 4636: 4633:Multilingual 4632: 4629: 4627: 4624: 4621: 4619: 4616: 4611: 4608: 4607: 4603: 4600: 4598: 4595: 4590: 4588: 4585: 4580: 4578:OpenAssistant 4577: 4576: 4573: 4570: 4568: 4565: 4560: 4558: 4555: 4550: 4548: 4545: 4544: 4540: 4537: 4535: 4532: 4527: 4525: 4522: 4517: 4514: 4513: 4510: 4507: 4504: 4501: 4496: 4494: 4491: 4486: 4483: 4482: 4478: 4475:Trained with 4474: 4471: 4468: 4466: 4461: 4459: 4456: 4451: 4449:Cerebras-GPT 4448: 4447: 4443: 4439: 4436: 4433: 4430: 4427: 4424: 4419: 4417: 4414: 4413: 4409: 4405: 4402: 4399: 4394: 4389: 4387: 4384: 4381:February 2023 4379: 4376: 4373: 4372: 4368: 4364: 4361: 4359: 4356: 4353: 4350: 4347:December 2022 4345: 4343: 4340: 4339: 4335: 4332: 4330: 4325: 4320: 4318: 4315: 4312:November 2022 4310: 4307: 4306: 4302: 4300:CC-BY-NC-4.0 4299: 4296: 4290: 4285: 4283: 4280: 4277:November 2022 4275: 4272: 4271: 4267: 4264: 4262: 4256: 4251: 4249: 4245: 4240: 4238: 4235: 4234: 4230: 4227: 4225: 4222: 4217: 4214: 4209: 4206: 4205: 4201: 4198: 4196: 4193: 4188: 4186: 4183: 4178: 4175: 4174: 4170: 4167: 4164: 4158: 4153: 4151: 4148: 4143: 4140: 4139: 4135: 4131: 4128: 4125: 4119: 4114: 4111: 4106: 4103: 4100: 4099: 4095: 4091: 4087: 4084: 4081: 4075: 4070: 4068: 4065: 4060: 4058: 4055: 4054: 4050: 4047: 4044: 4041: 4036: 4034: 4031: 4028:February 2022 4026: 4023: 4022: 4018: 4015: 4012: 4006:1.56T words, 4005: 4000: 3997: 3992: 3989: 3986: 3985: 3981: 3978: 3975: 3969: 3964: 3962: 3959: 3956:December 2021 3954: 3951: 3950: 3946: 3942: 3939: 3936: 3930: 3925: 3922: 3919:December 2021 3917: 3914: 3913: 3909: 3906: 3904: 3898: 3893: 3891: 3888: 3885:December 2021 3883: 3881: 3878: 3877: 3873: 3869: 3866: 3864: 3861: 3856: 3854: 3851: 3848:December 2021 3846: 3843: 3842: 3838: 3835: 3833: 3829:338.6 billion 3827: 3822: 3820: 3816: 3813: 3808: 3805: 3804: 3800: 3797: 3794: 3791: 3786: 3784: 3781: 3776: 3774: 3771: 3770: 3766: 3763:The first of 3762: 3759: 3757: 3754: 3749: 3747: 3744: 3739: 3736: 3735: 3731: 3727: 3724: 3721: 3715: 3710: 3707: 3702: 3700: 3697: 3696: 3692: 3689: 3686: 3679: 3674: 3672: 3669: 3666:February 2019 3664: 3662: 3659: 3658: 3654: 3651: 3648: 3642: 3637: 3635: 3632: 3627: 3625: 3622: 3621: 3617: 3614: 3612: 3609: 3606: 3603: 3598: 3596: 3593: 3592: 3588: 3584: 3581: 3576: 3570: 3565: 3563: 3560: 3555: 3553: 3550: 3549: 3545: 3541: 3538: 3535: 3533: 3528: 3526: 3523: 3518: 3516: 3513: 3512: 3508: 3505: 3502: 3499: 3496: 3493: 3490: 3487: 3486: 3483: 3479: 3469: 3460: 3456: 3447: 3442: 3432: 3428: 3424: 3422: 3418: 3414: 3409: 3400: 3395: 3385: 3383: 3382:Goldman Sachs 3378: 3377: 3366: 3364: 3359: 3347: 3343: 3341: 3340: 3333: 3324: 3322: 3318: 3312: 3310: 3305: 3301: 3297: 3295: 3291: 3281: 3278: 3277:cross-entropy 3273: 3269: 3265: 3240: 3235: 3231: 3227: 3214: 3210: 3206: 3196: 3193: 3189: 3184: 3170: 3150: 3130: 3110: 3090: 3070: 3042: 3032: 3027: 3008: 3005: 3000: 2995: 2992: 2989: 2985: 2979: 2976: 2971: 2968: 2954: 2951: 2942: 2927: 2925: 2921: 2917: 2913: 2912: 2906: 2905:The NTL Model 2902: 2898: 2897:George Lakoff 2894: 2890: 2884: 2882: 2878: 2877:hallucination 2874: 2873:training data 2870: 2866: 2861: 2859: 2855: 2851: 2846: 2835: 2833: 2829: 2824: 2822: 2817: 2813: 2808: 2806: 2777: 2774: 2771: 2768: 2765: 2728:average 2723: 2720: 2712: 2694: 2691: 2688: 2685: 2682: 2653:correct token 2639: 2636: 2633:average 2628: 2625: 2617: 2599: 2596: 2593: 2590: 2587: 2561:correct token 2550:average 2545: 2542: 2534: 2533: 2531: 2517: 2497: 2488: 2486: 2482: 2474: 2470: 2466: 2463: 2460: 2457: 2453: 2449: 2448: 2447: 2445: 2440: 2438: 2429: 2424: 2420: 2398: 2395: 2390: 2386: 2382: 2379: 2376: 2373: 2370: 2367: 2364: 2361: 2358: 2355: 2352: 2349: 2346: 2343: 2340: 2337: 2329: 2313: 2310: 2305: 2301: 2292: 2291: 2290: 2284: 2267: 2259: 2243: 2235: 2219: 2211: 2208: 2191: 2183: 2182: 2181: 2158: 2154: 2150: 2143: 2139: 2135: 2130: 2123: 2119: 2115: 2110: 2107: 2100: 2097: 2092: 2088: 2084: 2081: 2075: 2065: 2064:learning rate 2062: 2058: 2054: 2046: 2030: 2021: 2005: 1996: 1992: 1976: 1967: 1966: 1965: 1961: 1946: 1944: 1940: 1936: 1932: 1930: 1926: 1920: 1899: 1893: 1887: 1867: 1847: 1827: 1817: 1815: 1811: 1807: 1803: 1799: 1793: 1786:Multimodality 1783: 1780: 1778: 1774: 1769: 1768: 1762: 1753: 1750: 1748: 1744: 1740: 1734: 1732: 1728: 1724: 1720: 1717: 1713: 1712:ReAct pattern 1708: 1706: 1696: 1694: 1690: 1686: 1681: 1679: 1673: 1664: 1662: 1657: 1655: 1647: 1641:Training cost 1638: 1630: 1628: 1624: 1612: 1608: 1605: 1601: 1600: 1599: 1596: 1592: 1590: 1585: 1583: 1578: 1575: 1564: 1560: 1558: 1552: 1548: 1538: 1536: 1530: 1520: 1518: 1514: 1504: 1502: 1496: 1485: 1475: 1473: 1466: 1456: 1452: 1447: 1437: 1434: 1432: 1428: 1427:Shan language 1417: 1415: 1411: 1407: 1403: 1401: 1396: 1390: 1380: 1378: 1374: 1370: 1360: 1357: 1354: 1351: 1348: 1345: 1342: 1339: 1336: 1333: 1330: 1327: 1324: 1323: 1320: 1311: 1306: 1299: 1295: 1291: 1287: 1282: 1271: 1261: 1259: 1255: 1252:variants and 1251: 1246: 1244: 1240: 1236: 1232: 1228: 1223: 1220: 1218: 1214: 1210: 1206: 1202: 1198: 1194: 1190: 1186: 1181: 1179: 1175: 1171: 1167: 1163: 1159: 1150: 1146: 1144: 1139: 1136: 1127: 1118: 1109: 1107: 1103: 1099: 1095: 1091: 1087: 1083: 1079: 1075: 1071: 1067: 1063: 1059: 1055: 1051: 1047: 1043: 1039: 1034: 1032: 1028: 1024: 1020: 1016: 1012: 1008: 1003: 1001: 997: 993: 989: 985: 981: 977: 973: 961: 956: 954: 949: 947: 942: 941: 939: 938: 931: 928: 924: 921: 920: 919: 916: 914: 911: 910: 904: 903: 896: 893: 891: 888: 886: 883: 881: 878: 876: 873: 871: 868: 866: 863: 862: 856: 855: 848: 845: 843: 840: 838: 835: 833: 830: 828: 825: 823: 820: 818: 815: 813: 810: 809: 803: 802: 795: 792: 790: 787: 785: 782: 780: 777: 776: 770: 769: 762: 759: 757: 754: 752: 751:Crowdsourcing 749: 747: 744: 743: 737: 736: 727: 724: 723: 722: 719: 717: 714: 712: 709: 707: 704: 703: 700: 695: 694: 686: 683: 681: 680:Memtransistor 678: 676: 673: 671: 668: 664: 661: 660: 659: 656: 654: 651: 647: 644: 642: 639: 637: 634: 632: 629: 628: 627: 624: 622: 619: 617: 614: 612: 609: 605: 602: 601: 600: 597: 593: 590: 588: 585: 583: 580: 578: 575: 574: 573: 570: 568: 565: 563: 562:Deep learning 560: 558: 555: 554: 551: 546: 545: 538: 535: 533: 530: 528: 526: 522: 520: 517: 516: 513: 508: 507: 498: 497:Hidden Markov 495: 493: 490: 488: 485: 484: 483: 480: 479: 476: 471: 470: 463: 460: 458: 455: 453: 450: 448: 445: 443: 440: 438: 435: 433: 430: 428: 425: 423: 420: 419: 416: 411: 410: 403: 400: 398: 395: 393: 389: 387: 384: 382: 379: 377: 375: 371: 369: 366: 364: 361: 359: 356: 355: 352: 347: 346: 339: 336: 334: 331: 329: 326: 324: 321: 319: 316: 314: 311: 309: 306: 304: 302: 298: 294: 293:Random forest 291: 289: 286: 284: 281: 280: 279: 276: 274: 271: 269: 266: 265: 258: 257: 252: 251: 243: 237: 236: 229: 226: 224: 221: 219: 216: 214: 211: 209: 206: 204: 201: 199: 196: 194: 191: 189: 186: 184: 181: 179: 178:Data cleaning 176: 174: 171: 169: 166: 164: 161: 159: 156: 154: 151: 149: 146: 144: 141: 140: 134: 133: 126: 123: 121: 118: 116: 113: 111: 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