A large language model ( LLM ) is a type of computational model designed for natural language processing tasks such as language generation . As language models , LLMs acquire these abilities by learning statistical relationships from vast amounts of text during a self-supervised and semi-supervised training process.
61-427: LaMDA ( Language Model for Dialogue Applications ) is a family of conversational large language models developed by Google . Originally developed and introduced as Meena in 2020, the first-generation LaMDA was announced during the 2021 Google I/O keynote, while the second generation was announced the following year. In June 2022, LaMDA gained widespread attention when Google engineer Blake Lemoine made claims that
122-442: A neural network -powered chatbot with 2.6 billion parameters, which Google claimed to be superior to all other existing chatbots. The company previously hired computer scientist Ray Kurzweil in 2012 to develop multiple chatbots for the company, including one named Danielle. The Google Brain research team, who developed Meena, hoped to release the chatbot to the public in a limited capacity, but corporate executives refused on
183-528: A "hype cycle" initiated by researchers and the media. Lemoine's claims have also generated discussion on whether the Turing test remained useful to determine researchers' progress toward achieving artificial general intelligence , with Will Omerus of the Post opining that the test actually measured whether machine intelligence systems were capable of deceiving humans, while Brian Christian of The Atlantic said that
244-481: A 12-billion-parameter LLM computational cost is 72,300 A100-GPU -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 $ 80,000 and $ 1,600,000. 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
305-482: A few cases. For example, in the instruction "Write an essay about the main themes represented in Hamlet ," 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. The largest LLM may be too expensive to train and use directly. For such models, mixture of experts (MoE) can be applied,
366-438: A further LLM. 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 trained on it). Training of largest language models might need more linguistic data than naturally available, or that
427-415: A line of research pursued by Google researchers since 2017 to train models reaching up to 1 trillion parameters. Most results previously achievable only by (costly) fine-tuning, can be achieved through prompt engineering , although limited to the scope of a single conversation (more precisely, limited to the scope of a context window). In order to find out which tokens are relevant to each other within
488-484: 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. 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 outside
549-419: A matter of experimentation and domain-specific considerations. 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 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
610-446: A pair of pretrained language model and image encoder to perform better on visual question answering than models trained from scratch. Google PaLM model was fine-tuned into a multimodal model PaLM-E using the tokenization method, and applied to robotic control. LLaMA models have also been turned multimodal using the tokenization method, to allow image inputs, and video inputs. GPT-4 can use both text and image as inputs (although
671-464: A portmanteau of "Reason + Act", constructs an agent 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 before generating an action, which is then executed in
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#1732869607507732-665: A total of 1.56T words. The largest LaMDA model has 137B non-embedding parameters. On May 11, 2022, Google unveiled LaMDA 2, the successor to LaMDA, during the 2022 Google I/O keynote. The new incarnation of the model draws examples of text from numerous sources, using it to formulate unique "natural conversations" on topics that it may not have been trained to respond to. On June 11, 2022, The Washington Post reported that Google engineer Blake Lemoine had been placed on paid administrative leave after Lemoine told company executives Blaise Agüera y Arcas and Jen Gennai that LaMDA had become sentient . Lemoine came to this conclusion after
793-618: A visual guide. While quantized models are typically frozen, and only pre-quantized models are fine-tuned, quantized models can still be fine-tuned. Multimodality means "having several modalities", and a "modality" refers to a type of input or output, such as video, image, audio, text, proprioception , etc. There have been many AI models trained specifically to ingest one modality and output another modality, such as AlexNet for image to label, visual question answering for image-text to text, and speech recognition for speech to text. A common method to create multimodal models out of an LLM
854-419: Is available only via API with no offering of downloading the model to execute locally. But it was the 2022 consumer-facing browser-based ChatGPT that captured the imaginations of the general population and caused some media hype and online buzz. The 2023 GPT-4 was praised for its increased accuracy and as a "holy grail" for its multimodal capabilities. OpenAI did not reveal the high-level architecture and
915-432: Is finite, then fine-tuning may be done just once. If the number of tools can grow arbitrarily, as with online API services, then the LLM can be fine-tuned to be able to read API documentation and call API correctly. A simpler form of tool use is retrieval-augmented generation : the augmentation of an LLM with document retrieval . Given a query, a document retriever is called to retrieve the most relevant documents. This
976-468: 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. 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
1037-432: Is not jagged , 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. As an example, consider a tokenizer based on byte-pair encoding. In the first step, all unique characters (including blanks and punctuation marks ) are treated as an initial set of n -grams (i.e. initial set of uni-grams). Successively
1098-408: Is reserved for employees of non-business institutions such as schools, police, and hospitals. The definition of administrative leave may vary by institution. Individuals may also be eligible for administrative leave for various reasons including: bereavement, jury /court appearances, military leave, internal reviews, and investigations. In academic settings, administrative leaves are provided for
1159-490: 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 E {\displaystyle E} . Make a small multilayered perceptron f {\displaystyle f} , so that for any image y {\displaystyle y} , the post-processed vector f ( E ( y ) ) {\displaystyle f(E(y))} has
1220-594: Is usually done by encoding the query and the documents into vectors, then finding the documents with vectors (usually stored in a vector database ) 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. An LLM is typically not an autonomous agent by itself, as it lacks the ability to interact with dynamic environments, recall past behaviors, and plan future actions, but can be transformed into one by integrating modules like profiling, memory, planning, and action. The ReAct pattern ,
1281-576: The Shan language from Myanmar . Even more widespread languages such as Portuguese and German have "a premium of 50%" compared to English. Greedy tokenization also causes subtle problems with text completion. 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 training
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#17328696075071342-450: The chatbot had become sentient . The scientific community has largely rejected Lemoine's claims, though it has led to conversations about the efficacy of the Turing test , which measures whether a computer can pass for a human. In February 2023, Google announced Bard (now Gemini), a conversational artificial intelligence chatbot powered by LaMDA, to counter the rise of OpenAI 's ChatGPT . On January 28, 2020, Google unveiled Meena,
1403-941: The data on which they are trained. Before 2017, there were a few language models that were large as compared to capacities then available. In the 1990s, the IBM alignment models pioneered statistical language modelling. A smoothed n-gram model in 2001 trained on 0.3 billion words achieved state-of-the-art perplexity at the time. In the 2000s, as Internet use became prevalent, some researchers constructed Internet-scale language datasets ("web as corpus" ), upon which they 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. After neural networks became dominant in image processing around 2012, they were applied to language modelling as well. Google converted its translation service to Neural Machine Translation in 2016. As it
1464-606: The AI Test Kitchen app. In August, the app was delisted from Google Play and the Apple App Store , instead moving completely online. On February 6, 2023, Google announced Bard, a conversational AI chatbot powered by LaMDA, in response to the unexpected popularity of OpenAI 's ChatGPT chatbot. Google positions the chatbot as a "collaborative AI service" rather than a search engine . Bard became available for early access on March 21. In addition to Bard, Pichai also unveiled
1525-570: The Institute for Human-Centered Artificial Intelligence at Stanford University , and University of Surrey professor Adrian Hilton. Yann LeCun , who leads Meta Platforms ' AI research team, stated that neural networks such as LaMDA were "not powerful enough to attain true intelligence". University of California, Santa Cruz professor Max Kreminski noted that LaMDA's architecture did not "support some key capabilities of human-like consciousness" and that its neural network weights were "frozen", assuming it
1586-678: 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. 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 recurrent neural network variants and Mamba (a state space model). Because machine learning algorithms process numbers rather than text,
1647-482: 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. For Transformer-based LLM, training cost is much higher than inference cost. It costs 6 FLOPs per parameter to train on one token, whereas it costs 1 to 2 FLOPs per parameter to infer on one token. There are certain tasks that, in principle, cannot be solved by any LLM, at least not without
1708-439: The U.S. Constitution , comparing it to an "alien intelligence of terrestrial origin". He further revealed that he had been dismissed by Google after he hired an attorney on LaMDA's behalf, after the chatbot requested that Lemoine do so. On July 22, Google fired Lemoine, asserting that Blake had violated their policies "to safeguard product information" and rejected his claims as "wholly unfounded". Internal controversy instigated by
1769-462: The chatbot made questionable responses to questions regarding self-identity , moral values , religion, and Isaac Asimov 's Three Laws of Robotics . Google refuted these claims, insisting that there was substantial evidence to indicate that LaMDA was not sentient. In an interview with Wired , Lemoine reiterated his claims that LaMDA was "a person" as dictated by the Thirteenth Amendment to
1830-673: The company in frustration. Google announced the LaMDA conversational large language model during the Google I/O keynote on May 18, 2021, powered by artificial intelligence . The acronym stands for "Language Model for Dialogue Applications". Built on the seq2seq architecture, transformer -based neural networks developed by Google Research in 2017, LaMDA was trained on human dialogue and stories, allowing it to engage in open-ended conversations. Google states that responses generated by LaMDA have been ensured to be "sensible, interesting, and specific to
1891-540: The company's Generative Language API, an application programming interface also based on LaMDA, which he announced would be opened up to third-party developers in March 2023. LaMDA is a decoder-only Transformer language model. It is pre-trained on a text corpus that includes both documents and dialogs consisting of 1.56 trillion words, and is then trained with fine-tuning data generated by manually annotated responses for "sensibleness, interestingness, and safety". LaMDA
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1952-466: The context". LaMDA has access to multiple symbolic text processing systems , including a database, a real-time clock and calendar, a mathematical calculator, and a natural language translation system, giving it superior accuracy in tasks supported by those systems, and making it among the first dual process chatbots. LaMDA is also not stateless , because its " sensibleness " metric is fine-tuned by "pre-conditioning" each dialog turn by prepending many of
2013-593: The controversy was an instance of the ELIZA effect . With the unveiling of LaMDA 2 in May 2022, Google also launched the AI Test Kitchen, a mobile application for the Android operating system powered by LaMDA capable of providing lists of suggestions on-demand based on a complex goal. Originally open only to Google employees, the app was set to be made available to "select academics, researchers, and policymakers" by invitation sometime in
2074-413: 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 in the subsequent episodes. Monte Carlo tree search 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
2135-585: The environment to act as world model. 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 propose increasingly difficult tasks for curriculum learning . Instead of outputting individual actions, an LLM planner can also construct "skills", or functions for complex action sequences. The skills can be stored and later invoked, allowing increasing levels of abstraction in planning. LLM-powered agents can keep
2196-608: 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. 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. The Reflexion method constructs an agent that learns over multiple episodes. At
2257-608: The grounds that Meena violated Google's "AI principles around safety and fairness". Meena was later renamed LaMDA as its data and computing power increased, and the Google Brain team again sought to deploy the software to the Google Assistant , the company's virtual assistant software, in addition to opening it up to a public demo. Both requests were once again denied by company leadership. This eventually led LaMDA's two lead researchers, Daniel de Freitas and Noam Shazeer, to depart
2318-404: The incident prompted Google executives to decide against releasing LaMDA to the public, which it had previously been considering. Lemoine's claims were widely pushed back by the scientific community. Many experts rejected the idea that LaMDA was sentient, including former New York University psychology professor Gary Marcus , David Pfau of Google sister company DeepMind , Erik Brynjolfsson of
2379-404: The initial-set of uni-grams. 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 up to 15 times more tokens per word for some languages, for example for
2440-644: The leave, employers may investigate the situation before determining an appropriate course of action. Administrative leave does not in itself imply that an employee will be disciplined or that an allegation is credible, which is why pay and benefits are not discontinued. It simply allows the employer to investigate the incident, maintaining the employee's status while at the same time removing them from work, eventually leading to either their return or dismissal. Police officers are routinely placed on administrative leave while being investigated for alleged misconduct, but "nearly always get paid while they're being investigated,
2501-429: The model must predict whether they appear consecutively in the training corpus. During training, regularization loss is also used to stabilize training. However regularization loss is usually not used during testing and evaluation. Substantial infrastructure is necessary for training the largest models. Advances in software and hardware have reduced the cost substantially since 2020, such that in 2023 training of
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2562-489: 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) n -grams that most frequently occur together are then again merged into even lengthier n -gram, until a vocabulary of prescribed size is obtained (in case of GPT-3 , 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
2623-443: The most recent dialog interactions, on a user-by-user basis. LaMDA is tuned on nine unique performance metrics: sensibleness, specificity, interestingness, safety, groundedness, informativeness, citation accuracy, helpfulness, and role consistency. Tests by Google indicated that LaMDA surpassed human responses in the area of interestingness. The pre-training dataset consists of 2.97B documents, 1.12B dialogs, and 13.39B utterances, for
2684-552: The naturally occurring data is of insufficient quality. In these cases, synthetic data might be used. Microsoft's Phi series of LLMs is trained on textbook-like data generated by another LLM. Reinforcement learning from human feedback (RLHF) through algorithms, such as proximal policy optimization , is used to further fine-tune a model based on a dataset of human preferences. Using "self-instruct" approaches, LLMs have been able to bootstrap correct responses, replacing any naive responses, starting from human-generated corrections of
2745-539: The number of parameters of GPT-4. Competing language models have for the most part been attempting to equal the GPT series, at least in terms of number of parameters. Since 2022, source-available models have been gaining popularity, especially at first with BLOOM and LLaMA , though both have restrictions on the field of use. Mistral AI 's models Mistral 7B and Mixtral 8x7b have the more permissive Apache License . As of June 2024 , The Instruction fine tuned variant of
2806-453: 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. 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 ChatGPT ,
2867-566: The range of most consumer electronics. Post-training quantization 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 codebook per layer. Further improvement can be done by applying different precisions to different parameters, with higher precision for particularly important parameters ("outlier weights"). See for
2928-407: 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 frozen to improve stability. Flamingo demonstrated the effectiveness of the tokenization method, finetuning
2989-410: The same purpose as sabbaticals and research/study leaves, i.e., to allow individuals to improve themselves academically and to engage in research to foster their effectiveness as teachers and scholars. An employee may be placed on administrative leave when an allegation of misconduct is made against an employee, either by a co-worker, student, parent, an alleged victim, or a police officer. During
3050-475: 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 weights. For example, the small (i.e. 117M parameter sized) GPT-2 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
3111-527: 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 embedding is associated to the integer index. Algorithms include byte-pair encoding (BPE) and WordPiece . There are also special tokens serving as control characters , such as [MASK] for masked-out token (as used in BERT ), and [UNK] ("unknown") for characters not appearing in
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#17328696075073172-399: The time now? It is ", where a separate program interpreter would need to execute a code to get system time on the computer, so that the LLM can include it in its reply. This basic strategy can be sophisticated with multiple attempts of generated programs, and other sampling strategies. Generally, in order to get an LLM to use tools, one must fine-tune it for tool-use. If the number of tools
3233-464: The training with gradient descent a batch size of 512 was utilized. The largest models, such as Google's Gemini 1.5 , 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
3294-400: 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 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
3355-458: The vision component was not released to the public until GPT-4V ); Google DeepMind 's Gemini is also multimodal. Mistral introduced its own multimodel Pixtral 12B model in September 2024. The following four hyper-parameters characterize an LLM: Administrative leave Administrative leave is a temporary leave from a job assignment, with pay and benefits intact. Generally, the term
3416-589: 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. For example, the BPE tokenizer used by GPT-3 (Legacy) would split tokenizer: texts -> series of numerical "tokens" as Tokenization also compresses the datasets. Because LLMs generally require input to be an array that
3477-524: The year. In August, the company began allowing users in the U.S. to sign up for early access. In November, Google released a "season 2" update to the app, integrating a limited form of Google Brain's Imagen text-to-image model . A third iteration of the AI Test Kitchen was in development by January 2023, expected to launch at I/O later that year. Following the 2023 I/O keynote in May, Google added MusicLM, an AI-powered music generator first previewed in January, to
3538-700: Was retrieval-augmented to improve the accuracy of facts provided to the user. Three different models were tested, with the largest having 137 billion non-embedding parameters: Large language model The largest and most capable LLMs are artificial neural networks built with a decoder-only transformer-based architecture , enabling efficient processing and generation of large-scale text data. Modern models can be fine-tuned for specific tasks, or be guided by prompt engineering . These models acquire predictive power regarding syntax , semantics , and ontologies inherent in human language corpora, but they also inherit inaccuracies and biases present in
3599-408: Was a typical large language model. Philosopher Nick Bostrom noted however that the lack of precise and consensual criteria for determining whether a system is conscious warrants some uncertainty. IBM Watson lead developer David Ferrucci compared how LaMDA appeared to be human in the same way Watson did when it was first introduced. Former Google AI ethicist Timnit Gebru called Lemoine a victim of
3660-409: Was before transformers , it was done by seq2seq deep LSTM networks. At the 2017 NeurIPS conference, Google researchers introduced the transformer architecture in their landmark paper " Attention Is All You Need ". This paper's goal was to improve upon 2014 seq2seq technology, and was based mainly on the attention mechanism developed by Bahdanau et al. in 2014. The following year in 2018, BERT
3721-412: Was introduced and quickly became "ubiquitous". Though the original transformer has both encoder and decoder blocks, BERT is an encoder-only model. Although decoder-only GPT-1 was introduced in 2018, it was GPT-2 in 2019 that caught widespread attention because OpenAI at first deemed it too powerful to release publicly, out of fear of malicious use. GPT-3 in 2020 went a step further and as of 2024
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