What Is a Large Language Model?

A large language model (LLM) is a type of artificial intelligence model that has been trained through deep learning algorithms to recognize, generate, translate, and/or summarize vast quantities of written human language and textual data. Large language models are some of the most advanced and accessible natural language processing (NLP) solutions today.

As a form of generative AI, large language models can be used to not only assess existing text but to generate original content based on user inputs and queries.

Read on to learn more about large language models, how they work, and how they compare to other common forms of artificial intelligence.

Also see: Top Generative AI Apps and Tools

Large Language Models: Table of Contents

• Large Language Model Definition
• How Do Large Language Models Work?
• Types of Large Language Models
• What Are the Most Common Examples of Large Language Models?
• What Is the Purpose Behind Large Language Models?
• Bottom Line: The LLM and Generative AI

Large Language Model Definition

A large language model, otherwise known as an LLM, is an AI solution that can contextually learn data in sequence via specialized neural networks called transformers (see below for more on transformers.)

Through transformer-based training on massive training datasets, large language models can quickly comprehend and begin generating their own human language content. In many cases, large language models are also used for tasks like summarizing, translating, and predicting the next or missing sequence of text.

Also see: 100+ Top AI Companies 2023

Large Language Model vs. Natural Language Processing

Natural language processing (NLP) is a larger field of theory, computer science, and artificial intelligence that focuses on developing and enhancing machines that can understand and interpret natural language datasets.

The large language model is a specific application of natural language processing that moves beyond the basic tenets of textual analysis, using advanced AI algorithms and technologies to generate believable human text and complete other text-based tasks.

Large Language Model vs. Transformer Model

Simply stated, a large language model is a larger version of a transformer model in action. A transformer model is a type of neural network architecture that uses a concept called self-attention to stay on track and allow it to quickly and efficiently transform large numbers of inputs into relevant outputs.

Large language models are created through this transformer model architecture to help them focus on and understand large quantities of textual data.

More on this topic: Generative AI Companies: Top 12 Leaders

Large Language Model vs. Neural Networks

Large language models function through the use of specialized neural networks called transformer models.

In other words, a large language model is a type of neural network architecture that focuses primarily on understanding and generating original human-sounding content. Neural networks are advanced AI architectures that attempt to mimic the human brain in order to support more advanced outcomes.

Learn more: What Are Neural Networks?

Large Language Model vs. Generative AI

A large language model is a type of generative AI that focuses on generating human-like text in ways that make contextual sense. Generative AI is often used to generate text, but the technology can also be used to generate original audio, images, video, synthetic data, 3D models, and other non-text outputs.

On a related topic: What is Generative AI?

GPT vs. BERT

GPT and BERT are both transformer-based large language models, but they work in different ways.

GPT stands for Generative Pre-trained Transformer. It is an autoregressive type of language model that OpenAI manages for users who want to generate human-like text. BERT stands for Bidirectional Encoder Representations from Transformers; it is a collection of bidirectional language models from Google that is best known for its high levels of natural language and contextual understanding.

Because BERT is built on a transformer encoder with only an encoder stack, BERT is designed to generate and share all of its outputs at once. In contrast, GPT is a transformer decoder with only a decoder stack, so individual outputs can be shared based on previously decoded outputs. This difference in transformers means GPT models are better at generating new human-like text while BERT models are better at tasks like text classification and summarization.

Keep reading: ChatGPT vs. Google Bard: Generative AI Comparison

How Do Large Language Models Work?

Large language models work primarily through their specialized transformer architecture and massive training datasets.

For a large language model to work, it must first be trained on large amounts of textual data that make context, relationships, and textual patterns clear. This data can come from many sources, like websites, books, and historical records; Wikipedia and GitHub are two of the larger web-based samples that are used for LLM training. Regardless of its origin, training data must be cleansed and checked for quality before it is used to train an LLM.

Once the data has been cleansed and prepared for training, it’s time for it to be tokenized, or broken down into smaller segments for easier comprehension. Tokens can be words, special characters, prefixes, suffixes, and other linguistic components that make contextual meaning clearer. Tokens also inform a large language model’s attention mechanism, or its ability to quickly and judiciously focus on the most relevant parts of input text so it can predict and/or generate appropriate outputs.

Once a large language model has received its initial training, it can be deployed to users through various formats, including chatbots. However, enterprise users primarily access large language models through APIs that allow developers to integrate LLM functionality into existing applications.

The process of large language model training is primarily done through unsupervised, semi-supervised, or self-supervised learning. LLMs can adjust their internal parameters and effectively “learn” from new inputs from users over time.

Types of Large Language Models

There are many different transformer architectures and goals that inform the different types of large language models. While the types listed below are the main types you’ll see, keep in mind that many of these types overlap in specific model examples. For example, BERT is both autoencoding and bidirectional.

• Autoregressive: An autoregressive LLM uses past textual data and previous text inputs to determine the most sensical next word or phrase to add to a sequence. The earlier generations of OpenAI’s GPT technology are all examples of autoregressive models.
• Autoencoding: An autoencoding LLM takes an original input that is masked or corrupted and recreates the input so it’s visible to the user. Autoencoding models are used to identify missing text and context, especially when answering fill-in-the-blank questions or analyzing sentiment or context.
• Encoder-decoder: An encoder-decoder is a model type that can encode and decode both input and output tasks. An example of an encoder-decoder model is T5.
• Bidirectional: A model is bidirectional when it can learn and read information both from left to right and right to left. Many models can only read text and context from left to right, similar to how most English-speaking humans read a sentence.
• Fine-tuned: A fine-tuned LLM is typically a smaller LLM instance that has been trained to succeed at a more specific task and with more specific training data. Fine-tuned models can be used for everything from drug discovery to marketing content creation.
• Multimodal: An emerging type of LLM, multimodal large language models can accept other types of data inputs than text, such as images. One of the foremost examples of a multimodal model is OpenAI’s GPT-4.

What Are the Most Common Examples of Large Language Models?

Many of the biggest tech companies today work with some kind of large language model. While several of these models are only used internally or on a limited trial basis, tools like Google Bard and ChatGPT are quickly becoming widely available.

What Is the Purpose Behind Large Language Models?

Large language models are used to quickly interpret, contextualize, translate, and/or generate human-like content. Because of the transformer-based neural network architecture and massive training sets they rely on, large language models are able to create logical text outputs on nearly any scale for both personal and professional use cases. These are some of the most common purposes for large language models today:

• Generating original content
• Analyzing unstructured data
• Coding, code completion, and documentation
• Q&A
• Summarizing and translating content
• Problem-solving
• Conversation
• Customer support
• Recommendations
• Fraud detection

Learn about some of the top AI startups and their LLM solutions: Top Generative AI Startups

Bottom Line: The LLM and Generative AI

Although the large language model may not be the most advanced AI use case today, it is one of the most highly publicized and well-funded and is improving its capabilities by the minute.

The large language model is also one of the few useful applications of AI that the general public can access, especially through free research previews and betas like that offered for ChatGPT. Looking ahead — especially as more AI vendors refine and offer their LLMs to the public — expect to see these tools grow in features and functionality, generating higher-quality content based on more current and wide-ranging training data.

Read next: Top 9 Generative AI Applications and Tools

The post What Is a Large Language Model? appeared first on eWEEK.