What Are Large Language Models (LLMs)?

Key takeaways

• Large language models (LLMs) are AI models that learn patterns in human language from large amounts of data, allowing them to produce text, translate languages, answer questions, and more.

ChatGPT is a generative AI application that uses LLMs. This article explores uses for LLMs, how they work, who uses them, and their limitations. If you’re ready to build your AI skills, consider enrolling in IBM’s Generative AI Engineering with LLMs Specialization to learn in-demand, job-ready skills in gen AI, NLP apps, and large language models.

Large language models definition

Large language models (LLMs) are a type of artificial intelligence (AI) that uses machine learning algorithms to process and produce human language. They use massive data sets to develop their ability to translate languages, predict text, and generate content. As a type of model used in natural language processing (NLP), LLMs train on large data sets and use large numbers of parameters to learn complex patterns in human language.

How do large language models work?

At their core, LLMs are deep learning models based on neural networks, which are machine learning algorithms inspired by how the human brain works. LLMs start by breaking text into tokens, which can be words, parts of words, or punctuation marks. The model converts the tokens into numerical representations that the model can process.

To create the relationships between tokens in context, LLMs use multidimensional vectors to represent tokens in a mathematical space. Sentences form through the selection of tokens based on patterns the model learned during training.

LLMs often use self-supervised learning when it pre-trains on text data. After the initial training, models may undergo “fine-tuning” for specific use cases by training them on additional data and adjusting their parameters.

Learn more: What Is Prompt Engineering? And How to Write Effective Prompts

Is ChatGPT an LLM?

ChatGPT is a generative AI application developed by OpenAI that uses LLMs. OpenAI’s GPT models are generative pre-trained transformers trained on extensive amounts of text and other data. These models identify patterns and relationships in language, enabling ChatGPT to produce conversational text, translate languages, draft creative content, and answer questions [1].

Advantages and challenges of large language models

LLMs come with advantages and challenges when assessing their use in society. The EU AI Act is one of the world’s first AI laws, and it requires public and private organizations to support AI literacy among employees based on several factors, including knowledge and background [2]. Still, there are potential security and privacy concerns for anyone using the technology, especially when sharing sensitive or confidential company information.

Advantages of LLMs

With their ability to generate human-like text, LLMs offer several advantages:

Limitations of LLMs

LLMs have limitations related to their environmental impact, privacy, ethics, effects on the workforce, and potential for bias.

• Since LLMs are trained on large amounts of information from the internet, including personal information, people have raised privacy concerns involving the use of data captured and fed into the model.

• Human labor could fundamentally change with the full-scale implementation of LLMs, as many jobs could transform or become obsolete, creating challenges for workers in many fields.

How to get started with large language models

You can start interacting with AI tools that use large language models, such as ChatGPT from OpenAI or Google Gemini, to learn how they interact with you. Try giving different tools the same prompt and compare their responses. You may notice differences in the information they give you and how they respond to follow-up questions.

Many companies provide an LLM architecture and development frameworks you can use to create a customizable agent for your organization. When building an LLM, you can use retrieval augmented generation (RAG) to retrieve relevant information from an external knowledge source and give it to the LLM when creating a response. Training an LLM from scratch requires significant computing resources and large amounts of data, so many organizations prefer to customize or build applications with existing models.

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