Large Language Models: What They Are and How They Are Trained
Large language models (LLMs) are deep learning models trained on vast text data using transformer networks. They predict the next token in a sequence, learning patterns through self-attention and embeddings. This article explains their architecture, training steps, and applications.
What is a large language model?
Large language models are a deep learning category trained on enormous datasets to understand and generate natural language and other content. They rely on a transformer neural network architecture that excels at processing sequences of words and capturing patterns, as described in IBM's large language models overview.
According to Google Cloud's documentation, a large language model is a statistical model trained on massive amounts of data, capable of generating and translating text and performing other natural language processing tasks.
While natural language processing (NLP) is the broader field of enabling computers to understand language, large language models are a powerful subset distinguished by their massive size and ability to perform many tasks with minimal task-specific training, as noted in Hugging Face's LLM course introduction.
Transformer architecture
The transformer architecture, introduced by Google, uses two key innovations: positional encodings that embed the order of input, and self-attention that assigns weights to each part of the input data, allowing the model to focus on relevant context, as explained in NVIDIA's glossary entry.
Some transformer models consist of an encoder and a decoder with self-attention, which extract meanings from a sequence and understand relationships between words. Unlike earlier recurrent networks that process sequentially, transformers process entire sequences in parallel, enabling efficient training on GPUs, as described by AWS's LLM explanation.
Training process and tokenization
Tokenization is a crucial step: language models break text into tokens, which can be words, subwords, or characters. For example, the word 'unwatched' might be split into 'un', 'watch', and 'ed'. Tokens are the atomic units of language modeling, and modern models often use subword tokenization, as detailed in Google's Machine Learning Crash Course.
A language model calculates the likelihood of a given token or sequence of tokens appearing in a longer sequence. By modeling these probabilities, the model learns to predict the next token in a given context. This is done through training on large corpora, often using self-supervised learning where the model finds patterns without labeled data.
Scale, few-shot learning, and applications
The scale of these models is notable. For instance, GPT-3 has 175 billion parameters, a size that allows it to perform tasks with a few examples, known as few-shot learning, as reported in the arXiv paper.
Large language models can generate text, summarize documents, translate languages, classify content, and act as chatbots. They are flexible because a single model can handle multiple tasks, and they can be fine-tuned for specific applications.
| Capability | Example |
|---|---|
| Generation | Story writing, marketing content creation |
| Summarization | Legal paraphrasing, meeting notes summarization |
| Translation | Between languages, text-to-code |
| Classification | Toxicity classification, sentiment analysis |
| Chatbot | Open-domain Q+A, virtual assistants |
Frequently asked questions
What is a large language model?
A large language model is a deep learning model trained on vast text data using a transformer architecture. It predicts the next word or token in a sequence and can perform many natural language tasks.
How are large language models trained?
Training involves collecting a large dataset, tokenizing the text into units, converting tokens to embeddings, and using self-attention to learn patterns. The process is often unsupervised, using self-supervised learning.
What is tokenization in LLMs?
Tokenization splits text into words, subwords, or characters. These tokens are the smallest units the model processes. Subword tokenization helps handle rare or novel words consistently.
What is self-attention in transformers?
Self-attention assigns a weight to each input token based on its relevance to other tokens. It allows the model to capture relationships between distant words and process sequences in parallel.