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94-425: CanLII offers free public access to over 2.4 million documents across more than 300 case law and legislative databases. The official websites of provincial governments, which provide access to primary legislative documents, are linked to CANLII online. The CANLII database is one of the most comprehensive collections of Canadian federal, provincial and territorial legislation. It is used by lawyers, legal professionals and

188-520: A x ( ( h 0 d W Q ) ( H W K ) T ) ( H W V ) {\displaystyle c_{0}=\mathrm {Attention} (h_{0}^{d}W^{Q},HW^{K},HW^{V})=\mathrm {softmax} ((h_{0}^{d}W^{Q})\;(HW^{K})^{T})(HW^{V})} where the matrix H {\displaystyle H} is the matrix whose rows are h 0 , h 1 , … {\displaystyle h_{0},h_{1},\dots } . Note that

282-583: A query vector q 0 = h 0 d W Q {\displaystyle q_{0}=h_{0}^{d}W^{Q}} . Meanwhile, the hidden vectors outputted by the encoder are transformed by another linear map W K {\displaystyle W^{K}} into key vectors k 0 = h 0 W K , k 1 = h 1 W K , … {\displaystyle k_{0}=h_{0}W^{K},k_{1}=h_{1}W^{K},\dots } . The linear maps are useful for providing

376-539: A self-supervised and semi-supervised training process. 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

470-481: 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

564-486: A commentary program including law reviews, e-books, articles, public legal education materials, and reports. In June 2020, CanLII started actively promoting the CanLII guest writer program. As of February 2024, CanLII is piloting the use of a large language model to generate artificial intelligence case summaries. Other websites will often use CanLII as their primary source when referring to Canadian case law, and as of

658-500: A decoder network converts those vectors to sentences in the target language. The Attention mechanism was grafted onto this structure in 2014, and later refined into the Transformer design. Consider the seq2seq language English-to-French translation task. To be concrete, let us consider the translation of "the zone of international control <end>", which should translate to "la zone de contrôle international <end>". Here, we use

752-995: A dot-product attention mechanism, to obtain h 0 ′ = A t t e n t i o n ( h 0 W Q , H W K , H W V ) h 1 ′ = A t t e n t i o n ( h 1 W Q , H W K , H W V ) ⋯ {\displaystyle {\begin{aligned}h_{0}'&=\mathrm {Attention} (h_{0}W^{Q},HW^{K},HW^{V})\\h_{1}'&=\mathrm {Attention} (h_{1}W^{Q},HW^{K},HW^{V})\\&\cdots \end{aligned}}} or more succinctly, H ′ = A t t e n t i o n ( H W Q , H W K , H W V ) {\displaystyle H'=\mathrm {Attention} (HW^{Q},HW^{K},HW^{V})} . This can be applied repeatedly, to obtain

846-482: 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,

940-410: A fixed-length vector. (Xu et al 2015), citing (Bahdanau et al 2014), applied the attention mechanism as used in the seq2seq model to image captioning. One problem with seq2seq models was their use of recurrent neural networks, which are not parallelizable as both the encoder and the decoder must process the sequence token-by-token. Decomposable attention attempted to solve this problem by processing

1034-438: 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

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1128-415: 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

1222-484: 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

1316-419: 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

1410-614: A multilayered encoder. This is the "encoder self-attention", sometimes called the "all-to-all attention", as the vector at every position can attend to every other. For decoder self-attention, all-to-all attention is inappropriate, because during the autoregressive decoding process, the decoder cannot attend to future outputs that has yet to be decoded. This can be solved by forcing the attention weights w i j = 0 {\displaystyle w_{ij}=0} for all i < j {\displaystyle i<j} , called "causal masking". This attention mechanism

1504-446: 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

1598-464: 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

1692-565: A probability distribution over 0 , 1 , … {\displaystyle 0,1,\dots } . This can be accomplished by the softmax function , thus giving us the attention weights: ( w 00 , w 01 , … ) = s o f t m a x ( q 0 k 0 T , q 0 k 1 T , … ) {\displaystyle (w_{00},w_{01},\dots )=\mathrm {softmax} (q_{0}k_{0}^{T},q_{0}k_{1}^{T},\dots )} This

1786-477: A scene. These research developments inspired algorithms such as the Neocognitron and its variants. Meanwhile, developments in neural networks had inspired circuit models of biological visual attention. One well-cited network from 1998, for example, was inspired by the low-level primate visual system . It produced saliency maps of images using handcrafted (not learned) features, which were then used to guide

1880-455: A second neural network in processing patches of the image in order of reducing saliency. A key aspect of attention mechanism can be written (schematically) as: ∑ i ⟨ ( query ) i , ( key ) i ⟩ ( value ) i {\displaystyle \sum _{i}\langle ({\text{query}})_{i},({\text{key}})_{i}\rangle ({\text{value}})_{i}} where

1974-443: A sequence relative to the other components in that sequence. In natural language processing , importance is represented by "soft" weights assigned to each word in a sentence. More generally, attention encodes vectors called token embeddings across a fixed-width sequence that can range from tens to millions of tokens in size. Unlike "hard" weights, which are computed during the backwards training pass, "soft" weights exist only in

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2068-618: 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

2162-514: Is permutation equivariant in the sense that: By noting that the transpose of a permutation matrix is also its inverse, it follows that: which shows that QKV attention is equivariant with respect to re-ordering the queries (rows of Q {\displaystyle \mathbf {Q} } ); and invariant to re-ordering of the key-value pairs in K , V {\displaystyle \mathbf {K} ,\mathbf {V} } . These properties are inherited when applying linear transforms to

2256-455: Is aligned with the third word aime . Stacking soft row vectors together for je , t' , and aime yields an alignment matrix : Sometimes, alignment can be multiple-to-multiple. For example, the English phrase look it up corresponds to cherchez-le . Thus, "soft" attention weights work better than "hard" attention weights (setting one attention weight to 1, and the others to 0), as we would like

2350-419: 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

2444-970: Is computed with QKV attention as: head i = Attention ( Q W i Q , K W i K , V W i V ) {\displaystyle {\text{head}}_{i}={\text{Attention}}(\mathbf {Q} \mathbf {W} _{i}^{Q},\mathbf {K} \mathbf {W} _{i}^{K},\mathbf {V} \mathbf {W} _{i}^{V})} and W i Q , W i K , W i V {\displaystyle \mathbf {W} _{i}^{Q},\mathbf {W} _{i}^{K},\mathbf {W} _{i}^{V}} , and W O {\displaystyle \mathbf {W} ^{O}} are parameter matrices. The permutation properties of (standard, unmasked) QKV attention apply here also. For permutation matrices, A , B {\displaystyle \mathbf {A} ,\mathbf {B} } : from which we also see that multi-head self-attention :

2538-432: 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

2632-424: Is large, q 0 k 1 T {\displaystyle q_{0}k_{1}^{T}} is small, and the rest are very small. This can be interpreted as saying that the attention weight should be mostly applied to the 0th hidden vector of the encoder, a little to the 1st, and essentially none to the rest. In order to make a properly weighted sum, we need to transform this list of dot products into

2726-468: 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

2820-432: 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

2914-451: Is the "causally masked self-attention". The size of the attention matrix is proportional to the square of the number of input tokens. Therefore, when the input is long, calculating the attention matrix requires a lot of GPU memory. Flash attention is an implementation that reduces the memory needs and increases efficiency without sacrificing accuracy. It achieves this by partitioning the attention computation into smaller blocks that fit into

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3008-495: Is then lower triangular , with zeros in all elements above the diagonal. The masking ensures that for all 1 ≤ i < j ≤ n {\displaystyle 1\leq i<j\leq n} , row i {\displaystyle i} of the attention output is independent of row j {\displaystyle j} of any of the three input matrices. The permutation invariance and equivariance properties of standard QKV attention do not hold for

3102-475: Is then used to compute the context vector : c 0 = w 00 v 0 + w 01 v 1 + ⋯ {\displaystyle c_{0}=w_{00}v_{0}+w_{01}v_{1}+\cdots } where v 0 = h 0 W V , v 1 = h 1 W V , … {\displaystyle v_{0}=h_{0}W^{V},v_{1}=h_{1}W^{V},\dots } are

3196-490: 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

3290-610: Is used as a building block for an autoregressive decoder, and when at training time all input and output matrices have n {\displaystyle n} rows, a masked attention variant is used: Attention ( Q , K , V ) = softmax ( Q K T d k + M ) V {\displaystyle {\text{Attention}}(\mathbf {Q} ,\mathbf {K} ,\mathbf {V} )={\text{softmax}}\left({\frac {\mathbf {Q} \mathbf {K} ^{T}}{\sqrt {d_{k}}}}+\mathbf {M} \right)\mathbf {V} } where

3384-594: 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 ,

3478-576: 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

3572-941: 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

3666-596: The permutation invariance and permutation equivariance properties of QKV attention, let A ∈ R m × m {\displaystyle \mathbf {A} \in \mathbb {R} ^{m\times m}} and B ∈ R n × n {\displaystyle \mathbf {B} \in \mathbb {R} ^{n\times n}} be permutation matrices ; and D ∈ R m × n {\displaystyle \mathbf {D} \in \mathbb {R} ^{m\times n}} an arbitrary matrix. The softmax function

3760-521: The softmax function is applied independently to every row of its argument. The matrix Q {\displaystyle \mathbf {Q} } contains m {\displaystyle m} queries, while matrices K , V {\displaystyle \mathbf {K} ,\mathbf {V} } jointly contain an unordered set of n {\displaystyle n} key-value pairs. Value vectors in matrix V {\displaystyle \mathbf {V} } are weighted using

3854-417: The value vectors, linearly transformed by another matrix to provide the model with freedom to find the best way to represent values. Without the matrices W Q , W K , W V {\displaystyle W^{Q},W^{K},W^{V}} , the model would be forced to use the same hidden vector for both key and value, which might not be appropriate, as these two tasks are not

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3948-689: The 10th Edition of the Canadian Guide to Uniform Legal Citation , is the designated preferred citation , in the absence of official court-issued neutral citations . In November 2024, CanLII filed a lawsuit against Caseway , alleging that Caseway violated its terms of service by scraping content from CanLII's website. Large language model 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

4042-672: The GPU's faster on-chip memory, reducing the need to store large intermediate matrices and thus lowering memory usage while increasing computational efficiency. For matrices: Q ∈ R m × d k , K ∈ R n × d k {\displaystyle \mathbf {Q} \in \mathbb {R^{m\times d_{k}}} ,\mathbf {K} \in \mathbb {R^{n\times d_{k}}} } and V ∈ R n × d v {\displaystyle \mathbf {V} \in \mathbb {R^{n\times d_{v}}} } ,

4136-624: 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,

4230-482: 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

4324-887: The Query and Key vectors, where one item of interest (the Query vector "that") is matched against all possible items (the Key vectors of each word in the sentence). However, Attention's parallel calculations matches all words of a sentence with itself; therefore the roles of these vectors are symmetric . Possibly because the simplistic database analogy is flawed, much effort has gone into understand Attention further by studying their roles in focused settings, such as in-context learning, masked language tasks, stripped down transformers, bigram statistics, N-gram statistics, pairwise convolutions, and arithmetic factoring. Many variants of attention implement soft weights, such as For convolutional neural networks , attention mechanisms can be distinguished by

4418-661: The angled brackets denote dot product. This shows that it involves a multiplicative operation. Multiplicative operations within artificial neural networks had been studied under the names of Group Method of Data Handling (1965) (where Kolmogorov-Gabor polynomials implement multiplicative units or "gates" ), higher-order neural networks , multiplication units , sigma-pi units , fast weight controllers , and hyper-networks . In fast weight controller ( Schmidhuber , 1992), one of its two networks has "fast weights" or "dynamic links" (1981). A slow neural network learns by gradient descent to generate keys and values for computing

4512-436: The attention mechanism is more nuanced. On the first pass through the decoder, 94% of the attention weight is on the first English word I , so the network offers the word je . On the second pass of the decoder, 88% of the attention weight is on the third English word you , so it offers t' . On the last pass, 95% of the attention weight is on the second English word love , so it offers aime . As hand-crafting weights defeats

4606-406: The dimension on which they operate, namely: spatial attention, channel attention, or combinations. Much effort has gone into understand Attention further by studying their roles in focused settings, such as in-context learning, masked language tasks, stripped down transformers, bigram statistics, N-gram statistics, pairwise convolutions, and arithmetic factoring. These variants recombine

4700-399: The encoder has finished processing, the decoder starts operating over the hidden vectors, to produce an output sequence y 0 , y 1 , … {\displaystyle y_{0},y_{1},\dots } , autoregressively. That is, it always takes as input both the hidden vectors produced by the encoder, and what the decoder itself has produced before, to produce

4794-471: The encoder-side inputs to redistribute those effects to each target output. Often, a correlation-style matrix of dot products provides the re-weighting coefficients. In the figures below, W is the matrix of context attention weights, similar to the formula in Core Calculations section above. Self-attention is essentially the same as cross-attention, except that query, key, and value vectors all come from

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4888-413: 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

4982-585: 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

5076-608: 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

5170-411: The forward pass and therefore change with every step of the input. Earlier designs implemented the attention mechanism in a serial recurrent neural network language translation system, but a more recent design, namely the transformer , removed the slower sequential RNN and relied more heavily on the faster parallel attention scheme. Inspired by ideas about attention in humans , the attention mechanism

5264-435: The general public, with usage averaging over 30,000 visits per day. The case law database is reportedly growing at a rate of approximately 120,000 new cases each year, 20% of which are historic cases which are included to enrich existing databases. In April 2014, CanLII launched CanLII Connects, a legal community sourced publication and discussion platform for case law summaries and commentaries. In March 2018, CanLII launched

5358-497: The history of the attention mechanism are provided in Niu et al. and Soydaner. Selective attention in humans had been well studied in neuroscience and cognitive psychology. In 1953, Colin Cherry studied selective attention in the context of audition, known as the cocktail party effect . In 1958, Donald Broadbent proposed the filter model of attention . Selective attention of vision

5452-404: 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

5546-408: The input sequence in parallel, before computing a "soft alignment matrix" ( alignment is the terminology used by Bahdanau et al ) in order to allow for parallel processing. The idea of using the attention mechanism for self-attention, instead of in an encoder-decoder (cross-attention), was also proposed during this period, such as in differentiable neural computers and neural Turing machines . It

5640-434: The inputs and outputs of QKV attention blocks. For example, a simple self-attention function defined as: is permutation equivariant with respect to re-ordering the rows of the input matrix X {\displaystyle X} in a non-trivial way, because every row of the output is a function of all the rows of the input. Similar properties hold for multi-head attention , which is defined below. When QKV attention

5734-468: The mask, M ∈ R n × n {\displaystyle \mathbf {M} \in \mathbb {R} ^{n\times n}} is a strictly upper triangular matrix , with zeros on and below the diagonal and − ∞ {\displaystyle -\infty } in every element above the diagonal. The softmax output, also in R n × n {\displaystyle \mathbb {R} ^{n\times n}}

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5828-418: The masked variant. Multi-head attention MultiHead ( Q , K , V ) = Concat ( head 1 , . . . , head h ) W O {\displaystyle {\text{MultiHead}}(\mathbf {Q} ,\mathbf {K} ,\mathbf {V} )={\text{Concat}}({\text{head}}_{1},...,{\text{head}}_{h})\mathbf {W} ^{O}} where each head

5922-429: 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

6016-441: The model to make a context vector consisting of a weighted sum of the hidden vectors, rather than "the best one", as there may not be a best hidden vector. This view of the attention weights addresses some of the neural network explainability problem. Networks that perform verbatim translation without regard to word order would show the highest scores along the (dominant) diagonal of the matrix. The off-diagonal dominance shows that

6110-498: The model with enough freedom to find the best way to represent the data. Now, the query and keys are compared by taking dot products: q 0 k 0 T , q 0 k 1 T , … {\displaystyle q_{0}k_{0}^{T},q_{0}k_{1}^{T},\dots } . Ideally, the model should have learned to compute the keys and values, such that q 0 k 0 T {\displaystyle q_{0}k_{0}^{T}}

6204-489: 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

6298-552: 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

6392-419: The next output word: Here, we use the special <start> token as a control character to delimit the start of input for the decoder. The decoding terminates as soon as "<end>" appears in the decoder output. In translating between languages, alignment is the process of matching words from the source sentence to words of the translated sentence. In the I love you example above, the second word love

6486-539: 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

6580-453: 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 ,

6674-408: The purpose of machine learning, the model must compute the attention weights on its own. Taking analogy from the language of database queries , we make the model construct a triple of vectors: key, query, and value. The rough idea is that we have a "database" in the form of a list of key-value pairs. The decoder sends in a query , and obtains a reply in the form of a weighted sum of the values , where

6768-541: The querying vector, h 0 d {\displaystyle h_{0}^{d}} , is not necessarily the same as the key-value vector h 0 {\displaystyle h_{0}} . In fact, it is theoretically possible for query, key, and value vectors to all be different, though that is rarely done in practice. This attention scheme has been compared to the Query-Key analogy of relational databases. That comparison suggests an asymmetric role for

6862-566: 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

6956-407: 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

7050-478: The same model. Both encoder and decoder can use self-attention, but with subtle differences. For encoder self-attention, we can start with a simple encoder without self-attention, such as an "embedding layer", which simply converts each input word into a vector by a fixed lookup table . This gives a sequence of hidden vectors h 0 , h 1 , … {\displaystyle h_{0},h_{1},\dots } . These can then be applied to

7144-554: The same. This is the dot-attention mechanism. The particular version described in this section is "decoder cross-attention", as the output context vector is used by the decoder, and the input keys and values come from the encoder, but the query comes from the decoder, thus "cross-attention". More succinctly, we can write it as c 0 = A t t e n t i o n ( h 0 d W Q , H W K , H W V ) = s o f t m

7238-612: The scaled dot-product, or QKV attention is defined as: Attention ( Q , K , V ) = softmax ( Q K T d k ) V ∈ R m × d v {\displaystyle {\text{Attention}}(\mathbf {Q} ,\mathbf {K} ,\mathbf {V} )={\text{softmax}}\left({\frac {\mathbf {Q} \mathbf {K} ^{T}}{\sqrt {d_{k}}}}\right)\mathbf {V} \in \mathbb {R} ^{m\times d_{v}}} where T {\displaystyle {}^{T}} denotes transpose and

7332-475: 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

7426-612: The special <end> token as a control character to delimit the end of input for both the encoder and the decoder. An input sequence of text x 0 , x 1 , … {\displaystyle x_{0},x_{1},\dots } is processed by a neural network (which can be an LSTM, a Transformer encoder, or some other network) into a sequence of real-valued vectors h 0 , h 1 , … {\displaystyle h_{0},h_{1},\dots } , where h {\displaystyle h} stands for "hidden vector". After

7520-527: 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

7614-399: 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

7708-464: 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

7802-400: 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

7896-460: 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 performance of an LLM after pretraining largely depends on the: Attention (machine learning) Attention is a machine learning method that determines the relative importance of each component in

7990-589: 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

8084-403: The weight changes of the fast neural network which computes answers to queries. This was later shown to be equivalent to the unnormalized linear Transformer. A follow-up paper developed a similar system with active weight changing. During the deep learning era, attention mechanism was developed to solve similar problems in encoding-decoding. In machine translation, the seq2seq model, as it

8178-413: The weight is proportional to how closely the query resembles each key . The decoder first processes the "<start>" input partially, to obtain an intermediate vector h 0 d {\displaystyle h_{0}^{d}} , the 0th hidden vector of decoder. Then, the intermediate vector is transformed by a linear map W Q {\displaystyle W^{Q}} into

8272-444: The weights resulting from the softmax operation, so that the rows of the m {\displaystyle m} -by- d v {\displaystyle d_{v}} output matrix are confined to the convex hull of the points in R d v {\displaystyle \mathbb {R} ^{d_{v}}} given by the rows of V {\displaystyle \mathbf {V} } . To understand

8366-409: 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

8460-430: Was developed to address the weaknesses of leveraging information from the hidden layers of recurrent neural networks. Recurrent neural networks favor more recent information contained in words at the end of a sentence, while information earlier in the sentence tends to be attenuated . Attention allows a token equal access to any part of a sentence directly, rather than only through the previous state. Academic reviews of

8554-412: 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

8648-435: Was proposed in 2014, would encode an input text into a fixed-length vector, which would then be decoded into an output text. If the input text is long, the fixed-length vector would be unable to carry enough information for accurate decoding. An attention mechanism was proposed to solve this problem. An image captioning model was proposed in 2015, citing inspiration from the seq2seq model. that would encode an input image into

8742-406: Was studied in the 1960s by George Sperling 's partial report paradigm . It was also noticed that saccade control is modulated by cognitive processes, insofar as the eye moves preferentially towards areas of high salience . As the fovea of the eye is small, the eye cannot sharply resolve the entire visual field at once. The use of saccade control allows the eye to quickly scan important features of

8836-533: Was termed intra-attention where an LSTM is augmented with a memory network as it encodes an input sequence. These strands of development were brought together in 2017 with the Transformer architecture , published in the Attention Is All You Need paper. The seq2seq method developed in the early 2010s uses two neural networks: an encoder network converts an input sentence into numerical vectors, and

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