نموذج لغة كبير

نموذج لغة كبير أو النموذج اللغوي الكبير إنگليزية: Large language model (LLM) هو نموذج لغة يتكون من شبكة عصبونية تحتوي على عدد كبير من البارامترات (عادة ما يصل إلى مليارات الأوزان أو أكثر)، ويتم تدريبها على كميات كبيرة من النصوص غير الممتلئة باستخدام التعلم الذاتي المراقب أو شبه المراقب. ظهرت النماذج اللغة الكبيرة حوالي عام 2018 وتؤدي بشكل جيد في مجموعة واسعة من المهام. لقد تحول تركيز البحث في معالجة اللغات الطبيعية بعيداً عن المنهج السابق لتدريب نماذج مراقبة متخصصة للمهام المحددة.

على الرغم من عدم وجود تعريف رسمي لمصطلح نموذج لغة كبير، إلا أنه يشير عادة إلى النماذج التعلم العميق التي تحتوي على عدد من المعاملات يصل إلى مليارات أو أكثر.النماذج اللغوية الكبيرة هي نماذج عامة تتفوق في مجموعة واسعة من المهام، بدلاً من تدريبها لمهمة واحدة محددة (مثل تحليل المشاعر أو التعرف على الكيانات المسماة أو الحسابات الرياضية). يبدو أن الكفاءة التي تنجز بها المهام، والنطاق الذي تستطيع العمل فيه، يعتمد على كمية الموارد (البيانات، حجم البارامترات، القوة الحاسوبية) المخصصة لها، بطريقة لا تعتمد على تقنيات تصميم جديدة.

على الرغم من أنه تدرب على مهام بسيطة على غرار التنبؤ بالكلمة التالية في الجملة، وجد أن نموذج اللغة الكبير التي تم تدريبه بما يكفي وتحتوي على عدد كبير من المعاملات تقتنص الكثير من بنية ومعاني اللغة الإنسانية. بالإضافة إلى ذلك، يظهر نموذج اللغة الكبير معرفة عامة كبيرة حول العالم، وقادرة على "حفظ" كمية كبيرة من الحقائق خلال التدريب.

As language models, they work by taking an input text and repeatedly predicting the next token or word. Up to 2020, fine tuning was the only way a model could be adapted to be able to accomplish specific tasks. Larger sized models, such as GPT-3, however, can be prompt-engineered to achieve similar results. They are thought to acquire embodied knowledge about syntax, semantics and "ontology" inherent in human language corpora, but also inaccuracies and biases present in the corpora.

Notable examples include OpenAI's GPT models (e.g., GPT-3.5 and GPT-4, used in ChatGPT), Google's PaLM (used in Bard), and Meta's LLaMa, as well as BLOOM, Ernie 3.0 Titan, and Claude.

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 الميزات

 مجموعات بيانات التدريب المسبق

يعتمد نموذج اللغة الكبير على التدريب المسبق وذلك يستند إلى مجموعات بيانات نصية كبيرة. ومن بين مجموعات البيانات النصية التي يتم استخدامها بشكل شائع هي Common Crawl، The Pile، MassiveText، ويكي بيديا، وجيت هب. ويمكن الاطلاع على قائمة بأمثلة أكثر في قائمة مجموعات البيانات المستخدمة في بحوث التعلم الآلي على list of datasets for machine-learning research#الإنترنت. ويصل حجم بعض هذه المجموعات النصية إلى 10 تريليون كلمة.

يتراوح حجم البيانات اللغوية عالية الجودة بين 4.6 تريليون و17 تريليون كلمة، وهو يقع ضمن نطاق الحجم الذي يصل إليه أكبر مجموعات البيانات النصية بفارق مقداري.

 قوانين التحجيم

بشكل عام، يمكن توصيف نموذج لغة كبير بأربعة متغيرات: حجم النموذج، حجم مجموعة البيانات التدريبية، تكلفة التدريب، والأداء بعد التدريب. ويمكن تحديد كل من هذه المتغيرات الأربع برقم حقيقي، وتم العثور عليها تجريبياً على أنها مرتبطة بقوانين إحصائية بسيطة، تسمى "قوانين التوازن الحجمي".

مع ملاحظة أنه يمكن تحديد كل من هذه المتغيرات بدقة بأكثر من طريقة واحدة، مما يمنحنا العديد من القوانين المختلفة للتحجيم.

 تحجيم تشنتشلا

تشير قوانين التوازن الحجمي المختلفة إلى أنه، على سبيل المثال، يوجد قانون توازن حجمي معين (تسمى "تشنتشلا") ينص على أنه، في حالة تدريب نموذج لغة كبير تدريجياً للحصول على دورة واحدة، باستخدام جدول معدل التعلم الجيبي، ينطبق ما يلي:

$${\begin{cases}C=C_{0}ND\\L={\frac {A}{N^{\alpha }}}+{\frac {B}{D^{\beta }}}+L_{0}\end{cases}}$$

حيث أن المتغيرات

• $C$ C هو تكلفة تدريب النموذج بعمليات الفاصلة المتحركة في الثانية (فلوبس).
• $N$ عدد المتغيرات التي يجب تعديلها أثناء تدريب النموذج لتحقيق أفضل أداء.
• $D$ عدد الرموز (مثل الكلمات) الموجودة في مجموعة البيانات التي يتم استخدامها لتدريب النموذج.
• $L$ مقدار الخسارة الناتجة عن عدم القدرة على التنبؤ (وحدات المعلومات الطبيعية/الرموز) الصحيحة في مجموعة البيانات الاختبارية بواسطة النموذج، ويعرض هذا الإخراج عادة بصورة متوسطة لكل رمز في المجموعة.

والمتغيرات الإحصائية التي يتم استخدامها لوصف وتحليل البيانات

• $C_{0}=6$، وتشير إلى التكلفة اللازمة لتدريب النموذج على البيانات، وتشير إلى مقدار الفلوبس اللازمة لتدريب معامل واحد في النموذج، وتم تقدير هذا المقدار عادة من خلال حساب الفلوبس الإجمالي المستخدم في التدريب وتقسيمه على عدد المعاملات في النموذج. مع ملاحظة أن تكلفة التدريب أعلى بكثير من تكلفة الاستدلال، حيث يتكلف 1 إلى 2 فلوبس لكل معامل للإستدلال على رمز واحد
• $\alpha =0.34,\beta =0.28,A=406.4,B=410.7,L_{0}=1.69$.

تم تزويد قوانين إحصائية على البيانات التجريبية باستخدام $N\in [7\times 10^{7},1.6\times 10^{10}],D\in [5\times 10^{9},5\times 10^{11}],C\in [10^{18},10^{24}]$.

نظراً لوجود 4 متغيرات مرتبطة بمعادلتين، يسمح لنا فرض قيد إضافي وهدف أمثلي إضافي بحل جميع الأربعة المتغيرات. على وجه الخصوص، بالنسبة لأي قيمة ثابتة $C$، يمكننا حل الأربعة المتغيرات بشكل فريد الذي يقلل من $L$. هذا يوفر لنا الحل الأمثل $D_{opt}(C),N_{opt}(C)$ لأي قيمة ثابتة. $C$:

$$N_{opt}(C)=G\left({\frac {C}{6}}\right)^{a},\quad D_{opt}(C)=G^{-1}\left({\frac {C}{6}}\right)^{b},\quad {\text{ where }}\quad G=\left({\frac {\alpha A}{\beta B}}\right)^{\frac {1}{\alpha +\beta }},\quad a={\frac {\beta }{\alpha +\beta }}{\text{, and }}b={\frac {\alpha }{\alpha +\beta }}{\text{. }}$$

بعد توصيل القيم الرقمية، نحصل على حجم النموذج وحجم مجموعة بيانات التدريب الفعالة بنمط تشتشيلا، بالإضافة إلى الخسارة الممكنة في الاختبار:

$${\begin{cases}N_{opt}(C)=0.6\;C^{0.45}\\D_{opt}(C)=0.3\;C^{0.55}\\L_{opt}(C)=1070\;C^{-0.154}+1.7\end{cases}}$$

بالمثل، يمكننا العثور على الحجم الأمثل لمجموعة بيانات التدريب وميزانية الحسابات التدريبية الأمثلة لأي حجم محدد للنموذج، وهكذا.

هناك تقديرات أخرى لحجم النموذج وحجم مجموعة بيانات التدريب الفعالة. النص السابق يستند إلى نموذج إحصائي لـ "معامل تشتشيلا" والتي تعتمد على مجموعة بيانات محددة. $L={\frac {A}{N^{\alpha }}}+{\frac {B}{D^{\beta }}}+L_{0}$. يمكن أيضاً ملائمة قانون إحصائي مباشرة لـ "تكلفة التدريب" عوضًا عن استخدام نموذج إحصائي للحصول على الحجم الفعال للنموذج وحجم مجموعة بيانات التدريب الفعالة. $D_{opt}(C),N_{opt}(C)$ بدون الذهاب عبر مسار آخر، يتم الحصول على قيم مباشرة لـ "تكلفة التدريب" عوضاً عن الحصول على الحجم الفعال للنموذج وحجم مجموعة بيانات التدريب الفعالة:

$${\begin{cases}N_{opt}(C)=0.1\;C^{0.5}\\D_{opt}(C)=1.7\;C^{0.5}\end{cases}}$$

أو يتم توثيق القيم المحسوبة في شكل جدول:

$N_{opt}(C)$	$C$ / FLOP	$C$ / تكلفة تدريب Gopher بوحدة FLOPs.	$D_{opt}(C)$
400 مليون	1.92e+19	1/29968	8.0 مليار
1 مليار	1.21e+20	1/5706	20.2 مليار
10 مليار	1.23e+22	1/2819	205.1 مليار
67 مليار	5.76e+23	1	1.5 تريليون
175 مليار	3.85e+24	6.7	3.7 تريليون
280 مليار	9.90e+24	17.2	5.9 تريليون
520 مليار	3.43e+25	59.5	11.0 تريليون
1 تريليون	1.27e+26	221.3	21.2 تريليون
10 تريليون	1.30e+28	22515.9	216.2 تريليون

 Dataset preprocessing

انظر أيضاً: List of datasets for machine-learning research § Internet

 Dataset purification

Removal of toxic passages from the dataset, discarding low-quality data, and de-duplication are examples of dataset purification. Resulting, purified (high-quality) datasets contain up to 17 trillion words in 2022, raising from 985 million words, used in 2018 for GPT-1, and 3.3 billion words, used for BERT. The future data is, however, expected to be increasingly "contaminated" by LLM-generated contents themselves.

 Probabilistic tokenization

Using a modification of 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 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 repeatedly until a vocabulary of prescribed size is obtained (in case of GPT-3, the size is 50257). Token vocabulary consists of integers, spanning from zero up to the size of the token vocabulary. New words can always be interpreted as combinations of the tokens and the initial-set 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.

The more frequently the n-gram occurs in the dataset, the lower is the number used as its token; for example, the text tokenizer: texts -> series of numerical "tokens" may be split into:

n-grams:	token	izer	:	texts	->	series	of	numerical	"	t	ok	ens	"
numbers as "tokens":	30001	7509	25	13399	4613	2168	286	29052	366	83	482	641	1

Note that the last piece (i.e. " character) is encoded by the lowest number (i.e. 1) because it is the most frequent in the source dataset, while the next most frequent is the : character, encoded by number 25.

Probabilistic tokenization also compresses the datasets, which is the reason for using the byte pair encoding algorithm as a tokenizer. Because LLMs generally require input to be an array that 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.

 Training and architecture details

انظر أيضاً: Fine-tuning (machine learning)

 Reinforcement learning from human feedback (RLHF)

Reinforcement learning from human feedback (RLHF) through algorithms, such as proximal policy optimization, is used to furtherly fine-tune a model based on a dataset of human preferences.

 Instruction tuning

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

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 Prompt engineering, attention mechanism, and context window

انظر أيضاً: Prompt engineering and Attention (machine learning)

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).

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

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 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 token. 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.

The largest models can have a context window sized up to 32k (for example, GPT-4; while GPT-3.5 has a context window sized from 4k to 16k, and legacy GPT-3 has had 2k sized context window).

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 Chat-GPT, 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-sized include higher computational cost and possibly diluting the focus on local context, while making it smaller-sized can cause a model to miss an important long-range dependency. Balancing them are 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

• autoregressive (i.e. predicting how the segment continues, the way GPTs do it): for example given a segment "I like to eat", the model predicts "ice cream", or
• "masked" (i.e. filling in the parts missing from the segment, the way "BERT" does it): for example, given a segment "I like to [__] [__] cream", the model predicts that "eat" and "ice" are missing.

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, regularization loss is also used to stabilize training. However regularization loss is usually not used during testing and evaluation.

 Training cost

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 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 thousand and $1.6 million. Since 2020, large sums were invested into 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.

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.

 Tool use

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 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.

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

A simpler form of tool use is Retrieval Augmented Generation: augment an LLM with document retrieval, sometimes using a vector database. Given a query, a document retriever is called to retrieve the most relevant (usually measured by first encoding the query and the documents into vectors, then finding the documents with vectors closest in Euclidean norm to the query vector). The LLM then generates an output based on both the query and the retrieved documents.

 Agency

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 intelligent agent. Researchers have described several methods for such integrations.

The ReAct ("Reason+Act") method 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 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 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 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 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.

 Compression

Typically, LLM are trained with full- 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 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").

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

 Multimodality

Multimodality means "having several modalities", and a "modality" means a type of input, 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 review article of multimodal LLM is.

A common method to create multimodal models out of an LLM is to "tokenize" the output of a trained encoder. Concretely, one can construct a LLM that can understand images as follows: take a trained LLM, and take a trained image encoder $E$. Make a small multilayered perceptron $f$, so that for any image $y$, the post-processed vector $f(E(y))$ 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 finetuned 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 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 finetuned 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, while Google Gemini is expected to be multimodal.

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 Properties

 Scaling laws and emergent abilities

المقالة الرئيسية: Neural scaling law

The following four hyper-parameters characterize a LLM:

• cost of (pre-)training ($C$),
• size of the artificial neural network itself, such as number of parameters $N$ (i.e. amount of neurons in its layers, amount of weights between them and biases),
• size of its (pre-)training dataset (i.e. number of tokens in corpus, $D$),
• performance after (pre-)training.

They are related by simple statistical laws, called "scaling laws". One particular scaling law ("Chinchilla scaling") for LLM autoregressively trained for one epoch, with a log-log learning rate schedule, states that:

$${\begin{cases}C=C_{0}ND\\L={\frac {A}{N^{\alpha }}}+{\frac {B}{D^{\beta }}}+L_{0}\end{cases}}$$

where the variables are

• $C$ is the cost of training the model, in FLOPs.
• $N$ is the number of parameters in the model.
• $D$ is the number of tokens in the training set.
• $L$ is the average negative log-likelihood loss per token (nats/token), achieved by the trained LLM on the test dataset.

and the statistical hyper-parameters are

• $C_{0}=6$, 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.
• $\alpha =0.34,\beta =0.28,A=406.4,B=410.7,L_{0}=1.69$

At point(s) referred to as breaks, the lines change their slopes, appearing on a log-log plot as a series of linear segments connected by arcs.

When one subtracts out from the y-axis the best performance that can be achieved even with infinite scaling of the x-axis quantity, large models' performance, measured on various tasks, seems to be a linear extrapolation of other (smaller-sized and medium-sized) models' performance on a log-log plot. However, sometimes the line's slope transitions from one slope to another at point(s) referred to as break(s) in downstream scaling laws, appearing as a series of linear segments connected by arcs; it seems that larger models acquire "emergent abilities" at this point(s). These abilities are discovered rather than programmed-in or designed, in some cases only after the LLM has been publicly deployed.

The most intriguing among emergent abilities is in-context learning from example demonstrations. In-context learning is involved in tasks, such as:

• reported arithmetics, decoding the International Phonetic Alphabet, unscrambling a word's letters, disambiguate word in context, converting spatial words, cardinal directions (for example, replying "northeast" upon [0, 0, 1; 0, 0, 0; 0, 0, 0]), color terms represented in text.
• chain-of-thought prompting: 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.
• identifying offensive content in paragraphs of Hinglish (a combination of Hindi and English), and generating a similar English equivalent of Kiswahili proverbs.

Schaeffer et. al. argue that the emergent abilities are not unpredictably acquired, but predictably acquired according to a smooth scaling law. 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.

Let $x$ be the number of parameter count, and $y$ be the performance of the model.

* When $y={\text{average }}Pr({\text{correct token}})$, then $(\log x,y)$ is an exponential curve (before it hits the plateau at one), which looks like emergence.

• When $y={\text{average }}\log(Pr({\text{correct token}}))$, then the $(\log x,y)$ plot is a straight line (before it hits the plateau at zero), which does not look like emergence.
• When $y={\text{average }}Pr({\text{the most likely token is correct}})$, then $(\log x,y)$ is a step-function, which looks like emergence.

 Interpretation

Large language models by themselves are "black boxes", and it is not clear how they can perform linguistic tasks. There are several methods for understanding how LLM work.

Mechanistic interpretability aims to reverse-engineer 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 Othello 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 Karel programs. 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.

In another example, the authors trained small transformers on modular arithmetic addition. The resulting models were reverse-engineered, and it turned out they used discrete Fourier transform.

 Understanding and intelligence

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 "understand" 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 artificial general intelligence system": "Can one reasonably say that a system that passes exams for software engineering candidates is not really intelligent?" Some researchers characterize LLMs as "alien intelligence". For example, Conjecture CEO Connor Leahy considers untuned LLMs to be like inscrutable alien "Shoggoths", 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 [an unexpected] prompt, and suddenly you see this massive underbelly of insanity, of weird thought processes and clearly non-human understanding."

In contrast, some proponents of the "LLMs lack understanding" school believe that existing LLMs are "simply remixing and recombining existing writing", or 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 justified by their training data, a phenomenon which has been termed "hallucination". Neuroscientist Terrence Sejnowski has argued that "The diverging opinions of experts on the intelligence of LLMs suggests that our old ideas based on natural intelligence are inadequate".

 Evaluation

 Perplexity

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

$$\log({\text{Perplexity}})=-{\frac {1}{N}}\sum _{i=1}^{N}\log(Pr({\text{token}}_{i}|{\text{context for token}}_{i}))$$

here $N$ is the number of tokens in the text corpus, and "context for token $i$" depends on the specific type of LLM used. If the LLM is autoregressive, then "context for token $i$" is the segment of text appearing before token $i$. If the LLM is masked, then "context for token $i$" is the segment of text surrounding token $i$.

Because language models may overfit to their training data, models are usually evaluated by their perplexity on a test set 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.

 Task-specific datasets and benchmarks

A large number of testing datasets and benchmarks 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, commonsense reasoning, and mathematical problem-solving.

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 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.

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, ____".

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

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 prompts for particular tasks, particularly with respect to how many examples of solved tasks are adjoined to the prompt (i.e. the value of n in n-shot prompting).

 Adversarially constructed evaluations

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 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.

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 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 you can't teach an old dog new tricks, even though this is not literally true.

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 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:

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...
a) demonstrates how to increase efficient exercise work by running up and down balls.
b) moves all his arms and legs and builds up a lot of muscle.
c) then plays the ball and we see a graphics and hedge trimming demonstration.
d) performs sits ups while on the ball and talking.

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

 Wider impact

In 2023, Nature Biomedical Engineering 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 time." Goldman Sachs 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. 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 creators should exclude from their training data papers on creating or enhancing pathogens.

 List

Name	Release date	Developer	Number of parameters	Corpus size	Training cost (petaFLOP-day)	License	Notes
BERT	2018	Google	340000000 !340 million	3300000000 !3.3 billion words	9 !9	Apache 2.0	An early and influential language model, but encoder-only and thus not built to be prompted or generative
XLNet	2019	Google	340000000 !~340 million	3300000000 !33 billion words			An alternative to BERT; designed as encoder-only
GPT-2	2019	OpenAI	1500000000 !1.5 billion	40GB (~10000000000 !10 billion tokens)		MIT	general-purpose model based on transformer architecture
GPT-3	2020	OpenAI	175000000000 !175 billion	300000000000 !300 billion tokens	3640	proprietary	A fine-tuned variant of GPT-3, termed GPT-3.5, was made available to the public through a web interface called ChatGPT in 2022.
GPT-Neo	March 2021	EleutherAI	2700000000 !2.7 billion	825 GiB		MIT	The first of a series of free GPT-3 alternatives released by EleutherAI. GPT-Neo outperformed an equivalent-size GPT-3 model on some benchmarks, but was significantly worse than the largest GPT-3.
GPT-J	June 2021	EleutherAI	6000000000 !6 billion	825 GiB	200	Apache 2.0	GPT-3-style language model
Megatron-Turing NLG	October 2021	Microsoft and Nvidia	530000000000 !530 billion	338600000000 !338.6 billion tokens		Restricted web access	Standard architecture but trained on a supercomputing cluster.
Ernie 3.0 Titan	December 2021	Baidu	260000000000 !260 billion	4 Tb		Proprietary	Chinese-language LLM. Ernie Bot is based on this model.
Claude	December 2021	Anthropic	52000000000 !52 billion	400000000000 !400 billion tokens		beta	Fine-tuned for desirable behavior in conversations.
GLaM (Generalist Language Model)	December 2021	Google	1200000000000 !1.2 trillion	1600000000000 !1.6 trillion tokens	5600	Proprietary	Sparse mixture-of-experts model, making it more expensive to train but cheaper to run inference compared to GPT-3.
Gopher	December 2021	DeepMind	280000000000 !280 billion	300000000000 !300 billion tokens	5833	Proprietary
LaMDA (Language Models for Dialog Applications)	January 2022	Google	137000000000 !137 billion	1.56T words, 168000000000 !168 billion tokens	4110	Proprietary	Specialized for response generation in conversations.
GPT-NeoX	February 2022	EleutherAI	20000000000 !20 billion	825 GiB	740	Apache 2.0	based on the Megatron architecture
Chinchilla	March 2022	DeepMind	70000000000 !70 billion	1400000000000 !1.4 trillion tokens	6805	Proprietary	Reduced-parameter model trained on more data. Used in the Sparrow bot.
PaLM (Pathways Language Model)	April 2022	Google	540000000000 !540 billion	768000000000 !768 billion tokens	29250	Proprietary	aimed to reach the practical limits of model scale
OPT (Open Pretrained Transformer)	May 2022	Meta	175000000000 !175 billion	180000000000 !180 billion tokens	310	Non-commercial research	GPT-3 architecture with some adaptations from Megatron
YaLM 100B	June 2022	Yandex	100000000000 !100 billion	1.7TB		Apache 2.0	English-Russian model based on Microsoft's Megatron-LM.
Minerva	June 2022	Google	540000000000 !540 billion	38.5B tokens from webpages filtered for mathematical content and from papers submitted to the arXiv preprint server		Proprietary	LLM trained for solving "mathematical and scientific questions using step-by-step reasoning". Minerva is based on PaLM model, further trained on mathematical and scientific data.
BLOOM	July 2022	Large collaboration led by Hugging Face	175000000000 !175 billion	350000000000 !350 billion tokens (1.6TB)		Responsible AI	Essentially GPT-3 but trained on a multi-lingual corpus (30% English excluding programming languages)
Galactica	November 2022	Meta	120000000000 !120 billion	350000000000 !106 billion tokens	unknown	CC-BY-NC-4.0	Trained on scientific text and modalities.
AlexaTM (Teacher Models)	November 2022	Amazon	20000000000 !20 billion	1300000000000 !1.3 trillion		proprietary	bidirectional sequence-to-sequence architecture
LLaMA (Large Language Model Meta AI)	February 2023	Meta	65000000000 !65 billion	1400000000000 !1.4 trillion	6300	Non-commercial research	Trained on a large 20-language corpus to aim for better performance with fewer parameters. Researchers from Stanford University trained a fine-tuned model based on LLaMA weights, called Alpaca.
GPT-4	March 2023	OpenAI	Exact number unknown	Unknown	Unknown	proprietary	Available for ChatGPT Plus users and used in several products.
Cerebras-GPT	March 2023	Cerebras	13000000000 !13 billion		270	Apache 2.0	Trained with Chinchilla formula.
Falcon	March 2023	Technology Innovation Institute	40000000000 !40 billion	1 trillion tokens, from RefinedWeb (filtered web text corpus) plus some "curated corpora".	2800	Apache 2.0	Training cost around 2700 petaFLOP-days, 75% that of GPT-3.
BloombergGPT	March 2023	Bloomberg L.P.	50000000000 !50 billion	363 billion token dataset based on Bloomberg's data sources, plus 345 billion tokens from general purpose datasets		Proprietary	LLM trained on financial data from proprietary sources, that "outperforms existing models on financial tasks by significant margins without sacrificing performance on general LLM benchmarks"
پان‌گو	March 2023	هواوِيْ	1085000000000 !1.085 trillion	329 billion tokens		Proprietary
OpenAssistant	March 2023	LAION	17000000000 !17 billion	1.5 trillion tokens		Apache 2.0	Trained on crowdsourced open data
Jurassic-2	March 2023	AI21 Labs	Exact size unknown	Unknown		Proprietary	Multilingual
PaLM 2 (Pathways Language Model 2)	May 2023	Google	340000000000 !340 billion	3600000000000 !3.6 trillion tokens	85000	Proprietary	Used in Bard chatbot.
Llama 2	July 2023	Meta	70000000000 !70 billion	2000000000000 !2 trillion tokens		Llama 2 license	Successor of LLaMA.

 Further reading

• Jurafsky, Dan, Martin, James. H. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 3rd Edition draft, 2023.
• Phuong, Mary; Hutter, Marcus (2022). "Formal Algorithms for Transformers". arXiv:2207.09238 [cs.LG].
• Eloundou, Tyna; Manning, Sam; Mishkin, Pamela; Rock, Daniel (2023). "GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models". arXiv:2303.10130 [econ.GN].
• Eldan, Ronen; Li, Yuanzhi (2023). "TinyStories: How Small Can Language Models Be and Still Speak Coherent English?". arXiv:2305.07759 [cs.CL].
• Frank, Michael C. (27 June 2023). "Baby steps in evaluating the capacities of large language models". Nature Reviews Psychology (in الإنجليزية). 2 (8): 451–452. doi:10.1038/s44159-023-00211-x. ISSN 2731-0574. S2CID 259713140. Retrieved 2 July 2023.
• Zhao, Wayne Xin; et al. (2023). "A Survey of Large Language Models". arXiv:2303.18223 [cs.CL].
• Kaddour, Jean; et al. (2023). "Challenges and Applications of Large Language Models". arXiv:2307.10169 [cs.CL].

 See also

• Foundation models
• Generative AI
• Cognitive NLP