Basic concepts in NLP in Large Language Models (LLMs)
Large Language Models (LLMs)
A large language model is a type of computer program that can understand and generate text in a way that is similar to humans. It is trained on a large amount of text data and can be used for a variety of natural language processing tasks such as text generation, text completion, language translation, and text summarization.
Large language models are becoming increasingly popular in many new applications in artificial intelligence, such as chatbots, language translation, text summarization, text generation, and question answering. They are also used in many other field such as sentiment analysis, language modeling and so on. Their ability to understand and generate text allows them to interact with humans in a more natural and intuitive way, which makes them useful in a wide range of applications.
In simple words, a large language model is a powerful computer program that can understand and generate text, which can be used in many different applications to interact with humans in a natural way.
Text? We only know to work with numbers
When working with text in neural networks, it is necessary to convert words into numerical values so that the network can process and understand the text. One way to do this is through the use of word embeddings. Word embeddings provide a dense, continuous representation of words that captures the relationships between words in a more meaningful way than traditional one-hot encoding. This numerical representation is more efficient and expressive, and it allows the neural network to make predictions and understand the context of a sentence more effectively. In large language models, the embeddings are learned during the training process and they are used as input to the network.
One-hot encoding is a method to represent a categorical variable as a vector of binary values. It is a way to convert a categorical variable, such as a word or a number, into a numerical vector that can be used as input for a neural network.
In one-hot encoding, a categorical variable is represented by a vector with the same number of elements as the number of categories. Each element in the vector corresponds to one category. The element is set to 1 if the category is active for that specific example, otherwise it is set to 0.
For example, if we have a vocabulary of 5 words ("the", "cat", "sat", "on", "mat"), then to one-hot encode the word "cat", we would create a vector of 5 elements, with a 1 in the position corresponding to "cat" and 0s in all other positions. This vector would be [0,1,0,0,0].
One-hot encoding is simple and efficient, but it can be very sparse and doesn't capture any information about the relationships between words. That's why in large language models, word embeddings are used instead, which is a more efficient and expressive representation that captures the relationships between words in a more meaningful way.
Understanding word embeddings
Word embeddings are a way of representing words in a continuous, numerical format that a deep learning model can understand and use for tasks such as language translation or text classification. The idea is to convert words, which are discrete symbols, into continuous vectors that can be used as input for a neural network.
For example, the word "cat" might be represented as the vector [0.2, -0.5, 0.3], and the word "dog" as [0.4, 0.1, -0.2]. These vectors are learned by the model and can capture the meaning and context of the words.
One popular method for creating word embeddings is called "word2vec". This algorithm takes in a large corpus of text and trains a neural network to predict a target word based on its surrounding context. The resulting vectors for each word can capture similarities and relationships between words, such as "cat" and "kitten" having similar embeddings.
Another method is GloVe (Global vectors for Word Representation) which learn embeddings by co-occurrences of words in a dataset. That is, how often a word appears in the context of another word.
Embeddings are used in many NLP tasks such as Text Classification, Sentiment Analysis, Named Entity Recognition, Machine Translation etc.
word2vec
A simplified description of wor2vec implementation could be as follows:
Start by collecting a large corpus of text. This is the data that the model will be trained on.
Define the window size for the model. This is the number of words on either side of the target word that will be used as context. For example, if the window size is 2, the model will use the two words before and after the target word as context.
For each word in the corpus, create training examples by extracting the context words using the defined window size. For example, if the window size is 2, the context for the word "cat" in the sentence "the cat sat on the mat" would be ["the", "sat", "on"].
Train a neural network with a single hidden layer using the extracted context words as input and the target word as output. The goal of the network is to predict the target word given the context words.
After the training is done, the embedding of each word is the vector of weights of the final layer.
Now you can use the trained embeddings for various NLP tasks such as text classification, language translation, and others.
(Optional) You can fine-tune the embeddings on a task-specific dataset or use pre-trained embeddings for a task.
In the word2vec algorithm, the first layer of the neural network typically takes in a one-hot encoded vector as input. One-hot encoding is a way of representing a word as a vector of all zeros, with a single one in the position corresponding to the index of the word in a vocabulary.
For example, let's say we have a vocabulary of 10,000 words. The word "cat" would be represented as a one-hot encoded vector with 10,000 dimensions, where the value at the index corresponding to "cat" is 1 and all other values are 0.
The output of the network is also a one-hot encoded vector that represents the predicted context word or target word. The output layer of the network is typically a dense layer with the same number of neurons as the size of the vocabulary.
The loss function used in word2vec is usually the negative log likelihood loss, also known as the cross-entropy loss. This loss function compares the predicted probability distribution of the output with the true probability distribution of the target word and calculates the difference. The goal of training is to minimize this difference, hence the name "negative log likelihood."
The cross-entropy loss compares the predicted probability distribution of the output with the true probability distribution of the target word, and it is computed by summing up the negative logarithm of predicted probability for the true class.
In summary, the inputs of the first layer of word2vec are one-hot encoded vectors that represent the context words or target word, the output is also a one-hot encoded vector that represents the predicted context word or target word, and the loss function used is the negative log likelihood loss (cross-entropy loss).
An example of 'one-hot encoding' could be:
Let's use the sentence "The cat sat on the mat" as an example. Let's say we have a vocabulary of 6 words: ["The", "cat", "sat", "on", "the", "mat"].
Here's how we would one-hot encode each word in the sentence:
"The": [1, 0, 0, 0, 0, 0]
"cat": [0, 1, 0, 0, 0, 0]
"sat": [0, 0, 1, 0, 0, 0]
"on": [0, 0, 0, 1, 0, 0]
"the": [0, 0, 0, 0, 1, 0]
"mat": [0, 0, 0, 0, 0, 1]
As you can see, each word is represented as a vector of 6 dimensions, with a single 1 in the position corresponding to the index of the word in the vocabulary, and all other values are 0.
It's worth noting that in practice, the vocabulary can be much larger and one-hot encoding can be memory-intensive. To overcome this limitation, we can use sparse representation, such as sparse categorical cross-entropy loss in the training process, this way we don't need to store all the one-hot encoded vectors, but only the index of the word in the vocabulary.
Recurrent Neural Networks (RNN)
The training process for a large language model on a RNN would involve several steps:
Collect a large corpus of text data, which will be used to train the model.
Prepare the data by tokenizing it into words and/or characters, and converting them into numerical values using techniques such as one-hot encoding or word embeddings.
Define the RNN architecture, which typically involves an input layer, one or more hidden layers, and an output layer. The hidden layers are usually implemented using an RNN cell, such as a Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU) cell.
Train the model by feeding the prepared data into the input layer and training the model to predict the next word or character in the sequence. The model's parameters are updated using an optimization algorithm such as stochastic gradient descent (SGD) to minimize a predefined loss function, such as cross-entropy loss.
Repeat step 4 for multiple epochs, adjusting the model's parameters to minimize the loss function and improve the model's performance on the training data.
Fine-tune the model on a smaller dataset for a specific task, such as text classification or language translation, by adjusting the model's parameters to minimize a loss function specific to that task.
It's worth noting that training a large language model on a vanilla RNN can be computationally expensive, as the number of parameters in the model increases with the size of the corpus. Additionally, vanilla RNNs have a limitation in capturing long-term dependencies on the input sequence, which can lead to the vanishing or exploding gradients problem. To overcome these limitations, architectures such as LSTMs and GRUs have been developed which have a better performance when dealing with large datasets.
Tokens
In the context of a language model, a token refers to an atomic unit of meaning in text data. Tokens are the basic building blocks of natural language processing (NLP) tasks, such as language translation or text classification. Tokens can be words, characters, or subwords.
For example, in the sentence "The cat sat on the mat," the tokens would be "The", "cat", "sat", "on", "the", "mat". These tokens are the smallest units of meaning in the sentence, and a language model would be trained to understand and generate these tokens to perform NLP tasks.
Tokenization is the process of breaking down text into individual tokens. There are different ways of tokenizing text, such as word tokenization, where text is split into individual words, or character tokenization, where text is split into individual characters. The choice of tokenization method will depend on the task and the specific language model being used.
For example, when training a language model for text generation, subword tokenization is a popular approach, where the model is trained to generate subwords rather than individual words. This approach allows the model to generate text that can include out-of-vocabulary words, which can be particularly useful when working with languages with a large number of inflected forms or compounds.
In summary, a token in this context refers to an atomic unit of meaning in text data, the basic building blocks of NLP tasks, and it can be word, character or subword. Tokenization is the process of breaking down text into individual tokens, which are used as input to train a language model.
In GPT, 1 token is approximately 4 characters or 0.75 words for English text.
The 'cell' component in a RNN: internal memory (recurrence)
In a Recurrent Neural Network (RNN), the cell is the basic building block that allows the network to have memory. The cell is responsible for maintaining and updating the hidden state of the network, which captures information about the previous inputs in the sequence.
A cell in a RNN can be thought of as a small neural network that takes in the current input and the previous hidden state, and generates a new hidden state. The new hidden state is then passed on to the next time step as input along with the current input. This way the cell stores the information of the previous inputs and uses it to process the current input.
There are different types of cells used in RNNs, such as the SimpleRNN, LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) cells. Each of these cells has its own set of internal parameters, such as weights and biases, that are updated during training to optimize the performance of the network.
The SimpleRNN cell has a single layer and it updates the hidden state by applying a simple mathematical operation to the current input and the previous hidden state. LSTM and GRU cells have more complex architectures and use gating mechanisms to control the flow of information between the input, hidden state, and output. These cells help to overcome the issue of the vanishing gradients that simple RNNs have.
In summary, the cell in a RNN is the basic building block that allows the network to have memory by maintaining and updating the hidden state of the network. The hidden state captures information about the previous inputs in the sequence, which is used to process the current input. There are different types of cells used in RNNs, such as the SimpleRNN, LSTM, and GRU cells, each with its own set of internal parameters that are updated during training to optimize the performance of the network. LSTM and GRU cells have more complex architectures and use gating mechanisms to control the flow of information between the input, hidden state, and output, which helps to overcome the issue of vanishing gradients that simple RNNs have.
RNN in very basic words
Imagine you have a big box filled with a lot of words and sentences. This big box is called a language model.
We want to teach the big box to understand and generate words and sentences just like we do. To do that, we will use a special type of computer program called a Recurrent Neural Network (RNN).
The RNN is like a robot that can read and understand the words in the big box. It starts by reading the first word, and then it reads the next word, and so on. It also remembers the words it has read before. As it reads more words, it starts to understand the meaning of the sentences.
We also give the robot some examples of sentences we want it to learn. The robot reads those examples and remembers how the words are put together.
The robot keeps reading and learning for a long time, and as it does, it gets better and better at understanding and generating sentences. After a while, it becomes really good at it!
Now, when we ask the robot to write a sentence or generate a story, it can do it very well because it has learned from all the words and sentences in the big box.
In summary, a large language model is a big box filled with a lot of words and sentences, and a RNN is a special type of computer program that reads and understands the words in the big box and learns to generate sentences, like a robot that can read and learn.
Limitations of RNN and the 'transformer' architecture
Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are both types of Recurrent Neural Networks (RNNs) that are commonly used to overcome some of the limitations of vanilla RNNs. However, they do have some limitations of their own:
Computationally Expensive: LSTMs and GRUs have a larger number of parameters than vanilla RNNs, which can make training computationally expensive, especially when working with large datasets.
Limited Memory: Even though LSTMs and GRUs have been designed to overcome the problem of limited memory of vanilla RNNs, they still have a limited capacity to store information in their hidden states, which can be a problem when dealing with large or complex input sequences.
Overfitting: LSTMs and GRUs can be prone to overfitting, especially when trained on small datasets. This happens when the model becomes too specialized to the training data and performs poorly on new, unseen data.
Difficulty to parallelize: LSTMs and GRUs are sequential models, which means that they process the input sequence one step at a time. This can make them difficult to parallelize, which can make training on large datasets slower.
Requires a lot of data: LSTMs and GRUs require a lot of data to achieve good performance, which can be a limitation in some scenarios where data is scarce or expensive to acquire.
Complexity of tuning the parameters: LSTMs and GRUs have a lot of hyperparameters and internal parameters that need to be fine-tuned in order to achieve optimal performance. The process of tuning these parameters can be complex and time-consuming, and requires a good understanding of the underlying architecture.
Limited interpretability: LSTMs and GRUs are complex models, and it can be difficult to understand how they are making decisions. This can make it challenging to interpret the results of the model, especially when working on high-stakes applications such as medical diagnosis or financial fraud detection.
In summary, LSTMs and GRUs are powerful models that can overcome some of the limitations of vanilla RNNs, but they do have limitations of their own, such as computational expense, limited memory, overfitting, difficulty to parallelize, requires a lot of data, complexity of tuning the parameters and limited interpretability.
The 'transformer' architecture
The Transformer architecture is a solution to the limitations of previous attempts to implement large language models, particularly those based on Recurrent Neural Networks (RNNs). The main innovations of the Transformer are the attention mechanism and the ability to process the input sequence in parallel.
The attention mechanism is a mechanism that allows the model to weight different parts of the input sequence differently when making predictions. This allows the model to focus on the most relevant parts of the input sequence, which can be particularly useful when working with long input sequences.
The ability to process the input sequence in parallel is achieved by using self-attention mechanism. The self-attention mechanism allows the model to look at all the input elements at once, and compute the relationships between them, before doing the prediction. This allows the model to make predictions based on the entire input sequence at once, rather than processing it one element at a time, which can be particularly useful when working with long input sequences.
The Transformer architecture was introduced in the paper "Attention Is All You Need" by Google researchers in 2017. This architecture was used in the GPT-1 and GPT-2 models developed by OpenAI and it has been used in many other models such as BERT, RoBERTa, T5, etc.
In summary, The Transformer architecture is a solution to the limitations of previous attempts to implement large language models, particularly those based on Recurrent Neural Networks (RNNs) by introducing the attention mechanism and the ability to process the input sequence in parallel using self-attention mechanism.
How it works
representation, applies the transformer layers and then converts the output back into a token representation. This allows the transformer architecture to understand the context of the input sequence and make predictions based on the entire input sequence at once, which makes it well suited to processing long input sequences and improve the performance of large language models.
In summary, the transformer architecture is a neural network architecture that uses a multi-head attention mechanism to weight different parts of the input sequence differently, position encoding to understand the order of the tokens, layer normalization to stabilize the training process and improve the performance of the model, and feed-forward neural network to produce the output. Additionally, it tokenizes the input sentence, converts the tokens into numerical
Tokenization: The first step is to tokenize the input sentence into individual words or subwords. For this example, the tokens would be ["the", "cat", "is", "sat", "on", "the", "mat"].
Input Embedding: The next step is to convert the tokens into a numerical representation, called an embedding. This can be done using a technique called one-hot encoding, where each token is represented as a vector of zeros with a single 1 at the index corresponding to the token.
Position Encoding: The transformer architecture also uses something called position encoding. This is a technique where each token is also represented by a vector that encodes its position in the input sequence. This allows the model to understand the order of the tokens in the input sequence.
Multi-Head Attention: The transformer architecture uses a mechanism called multi-head attention. This mechanism allows the model to weight different parts of the input sequence differently when making predictions. The model computes multiple dot-product attention mechanisms in parallel, each with their own set of learnable parameters, then concatenates the results and pass through a feed-forward neural network.
Feed-forward Layer: The output of the multi-head attention mechanism is then passed through a feed-forward neural network. This layer applies a linear transformation to the input and applies a non-linear activation function to produce the output.
Layer Normalization: The transformer architecture also uses something called layer normalization. This is a technique where the output of each layer is normalized to have zero mean and unit variance. This helps to stabilize the training process and improve the performance of the model.
Output Embedding: The final step is to convert the output of the transformer architecture back into a token representation. This can be done by looking up the appropriate token in a vocabulary table using the index of the largest value in the output vector.
Softmax: The final output is passed through a softmax layer in order to get the probability distribution of the next word. The next word is chosen by sampling from this distribution.
In summary:
Ok, so imagine you have a big box filled with a lot of words and sentences like a dictionary, and you want to teach a computer to understand and generate words and sentences just like we do. To do that, we use a special type of computer program called the Transformer.
The Transformer is like a robot that can read and understand the words in the big box. It starts by reading the first word, and then it reads the next word and so on. It also remembers the words it has read before and understands the meaning of the sentences. But, unlike other robots, it can read and understand the whole sentence at once, instead of word by word.
We also give the robot some examples of sentences we want it to learn. The robot reads those examples and remembers how the words are put together and how they relate to each other.
The robot keeps reading and learning for a long time, and as it does, it gets better and better at understanding and generating sentences. After a while, it becomes really good at it! Now, when we ask the robot to write a sentence or generate a story, it can do it very well because it has learned from all the words and sentences in the big box.
In summary, the Transformer is a special type of computer program that can read and understand the words in the big box and learn to generate sentences, but it can read and understand the whole sentence at once, which makes it well suited to processing long input sequences and improve the performance of large language models.
GPT as one of the many 'transformer' based architectures
GPT stands for Generative Pre-trained Transformer. It is a type of large language model that is pre-trained on a massive amount of text data. The GPT model is trained to predict the next word in a sentence, given the previous words.
To train the GPT model, a huge amount of text data is fed into the model. The model learns the patterns and relationships between words in the text data. It also learns to predict the next word in a sentence based on the context of the previous words.
Once the GPT model is trained, it can be used for a variety of natural language processing tasks such as text generation, text completion, language translation, text summarization, and more.
The main use case of GPT models is to generate human-like text, they are also useful for language understanding tasks such as question answering, dialogue systems, and text classification.
To fine-tune a GPT model, it can be trained further on a smaller dataset specific to a particular task. This allows the model to adapt to the specific language and style of the new dataset.
Zero-shot and few-shot learning are concepts related to the ability of a model to perform a task without having seen any or very few examples of that task during the training process. GPT models are able to perform zero-shot and few-shot learning because they have been pre-trained on a large amount of data, which allows them to generalize to new tasks even if they haven't seen examples of that task before.
In summary, GPT models are large language models that are pre-trained on a massive amount of text data, they can be used for a variety of natural language processing tasks, they can be fine-tuned for specific tasks, and they are able to perform zero-shot and few-shot learning.
The training process for GPT models is generally unsupervised. The model is trained on a large dataset of text data, and it learns the patterns and relationships between words in the text without any explicit supervision or labels. The training objective is to predict the next word in a sentence, given the previous words. The model is trained to maximize the likelihood of the correct word given the context.
This is in contrast to supervised learning, where the model is trained on labeled data, and the training objective is to predict the correct label given the input. In supervised learning, the model is trained to minimize the difference between its predicted output and the correct output.
It's worth noting that some variants of GPT models, such as GPT-2, have fine-tuning capabilities where the model is fine-tuned on a smaller, labeled dataset for specific tasks, such as text classification or language understanding task. In this case, the fine-tuning process can be considered as a supervised learning process.
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