What Is a Large Language Model? A Developer's Reference
A large language model, or LLM, is a machine learning model that can perform various natural language processing (NLP) tasks, like translating texts, answering questions conversationally, and classifying and generating words based on the knowledge gained from different datasets. The term "large" here refers to the number of parameters used in its architecture, with some of the most common LLMs having billions of them.
The simplest way to define an LLM is as a model trained on a large corpus of data to understand human language. The model ingests Data from the internet, or proprietary corporate data, and the algorithm predicts the next word most likely should be. As a result, these language models have become increasingly popular for a range of NLP tasks.
Key Features of LLMs and How They Work
Most current LLMs are based on transformer architectures and use a self-attention mechanism to capture the dependencies between words, allowing them to understand contexts. It also uses autoregressive generation to produce text based on previously generated words called tokens.
Let's break all these down to understand better how a large language model works.
Machines that can comprehend text usually use a model based on recurrent neural networks or RNNs. This model processes one word at a time and recursively captures the relationship between words, or "tokens," in a sequence. However, it often needs to remember the beginning of the sequence as it reaches the end. This is where transformer-based architecture comes in.
Unlike RNNs, transformer neural networks that lie at the heart of most language processing models use self-attention to capture relationships.
Unlike recurrent neural networks that see a sentence or paragraph one word at a time, the attention mechanism allows the model to see the whole sentence simultaneously. This allows the model to understand the context better. Most language processing models follow the transformer architecture that uses the attention mechanism. Some LLMs combine both of these with autoregressive generation.
A transformer model processes text input by tokenizing it into a sequence of words. Then, the tokens are encoded as numbers and transformed into embeddings. Think of embeddings as vector-space representations of these tokens and their syntactic and semantic information.
Next, an encoder transforms the input embeddings into a context vector by analyzing the input and creating hidden states that capture its meaning and context. The context vector is what the decoder in the transformer uses to generate the output. The decoder enables autoregressive generation, where the model uses previously generated tokens to generate sequential outputs. This process is repeated to produce the entire paragraph, with the leading sentence as the starting point. This is how a large language model works.
What Are LLMs Used For?
As mentioned earlier, an LLM can be used in various ways in many industries, including the following:
- Conversational chatbots that can answer frequently asked questions 24/7 for better customer service
- Text generation for articles, blogs, and product descriptions, especially for e-commerce store stores
- Translating content into different languages to reach a wider audience
- Sentiment analysis to analyze customer feedback from product reviews, social media posts, and emails and to understand the intent of different pieces of content.
- Summarizing and rewriting blocks of text
- Categorizing and classifying text for more efficient analysis and processing
Some of the most common large language models include the following:
Developed by Google, Bidirectional Encoder Representations from Transformers (BERT) is a famous LLM with two model sizes. While the BERT base model has 110 million parameters, the BERT large model has 340 million. Like other LLMs, it can understand contexts and produce meaningful responses. BERT can also be used for generating embeddings for text.
Generative Pretrained Transformer 3, or GPT-3, is arguably the most popular LLM, partly due to ChatGPT, which is based on GPT-3.5 and GPT-4. The numbers, in this case, denote the version of the model, with GPT-3 being the third. This is one of the largest LLMs. OpenAI developed it and has 175 billion parameters.
RoBERTa stands for Robustly Optimized BERT Approach. It's an improved version of Google's BERT model developed by Meta AI (formerly Facebook Artificial Intelligence Research, or FAIR). Thanks to a higher parameter count, RoBERTa performs better on many language tasks. Just like BERT, RoBERTa also has two model sizes. The base version has 123 million parameters, while the large version has 354 million parameters.
Open-source LLMs have made it easier for developers, businesses, and researchers to build applications that use these models for free. One example of such an LLM is BLOOM. It's the first LLM that involved the most significant collaboration of AI researchers in a project and is trained in full transparency. It was trained on 1.6 terabytes of data, has 176 billion parameters, and can generate output in 13 programming and 46 natural languages.
Another LLM developed by Google is T5, or Text-to-Text Transfer Transformer, which is trained on various language tasks. Its base version has 220 million parameters, while the large version has 770 million parameters.
Frequently Asked Questions about LLMs
How Do Large Language Models Work?
Large language models are based on the transformer architecture and use self-attention to capture relationships between words or "tokens." They compute a weighted sum for an input and determine how the tokens in the input relate to each other. Attention scores are then used to calculate the relationships between tokens, and autoregressive generation is used to produce the output based on a given input. Most LLMs are trained on vast amounts of textual data available on the internet, but you can also feed them proprietary enterprise data to serve your customers better.
What Is the Difference Between Natural Language Processing and Large Language Models?
Natural language processing (NLP) is a field of artificial intelligence that focuses on processing and understanding human language. Meanwhile, a large language model refers to a model within NLP that can perform various language-related tasks, such as answering questions, summarizing text, and translating sentences from one language to another.
How Do I Create a Large Language Model?
Creating a large language model from scratch involves training it on a massive corpus of data with billions of parameters. This means you need to have an infrastructure with multiple GPUs that supports parallel and distributed computing. Setting this up can be expensive, so most researchers start making an LLM with an existing LLM architecture and its hyperparameters, such as GPT-3. Then, they tweak the hyperparameters, dataset, and architecture to create a new LLM.
What Is Generative AI vs Large Language Models?
"Generative AI" is an umbrella term that refers to a collection of algorithms that can dynamically generate output once it's trained. The distinguishing feature of generative AI is its ability to produce complex output forms, like images, code, poems, etc. Examples of generative AI include DALL-E, ChatGPT, Bard, Midjourney, and MusicLM.
A large language model is a generative AI. Unlike DALL-E, ChatGPT, and other generative AI tools, large language models are trained on text data and produce new text that can be used for various purposes.