What Are Large Language Models (LLMs)?

Large language models (LLMs) are a type of artificial intelligence (AI) that uses machine learning algorithms to replicate human language. They use massive data sets to develop their ability to translate languages, predict text, and generate content. As opposed to natural language processing models (NLPs), LLMs train on much larger data sets, allowing them to use a greater number of parameters to become more complex and closer to human language. 

As LLMs become more complex and human-like, they raise more ethical questions about their diversity, energy requirements, ability to make decisions, and use as content creators. This article examines the uses for LLMs, how they work, who uses them, their limitations, and how you can use them. If you're ready to build your AI skills, consider enrolling in IBM's Generative AI Engineering with LLMs Specialization, where you'll learn in-demand, job-ready skills in gen AI, NLP apps, and large language models.

How do large language models work?

At their core, LLMs are deep learning models based on neural networks, machine learning algorithms that attempt to replicate human neural activity. LLMs start by using tokens, which are words broken into numerical representations.

To create the relationships between words in contextual examples, LLMs use vectors in three-dimensional space to create relationships and, thus, sentences by decoding and recoding meaning. Sentences form through the selection of tokens based on statistics performed during its training. 

LLMs often use unsupervised learning and unstructured data to access mass quantities of data. After the initial training, models undergo “fine-tuning” if they require specific use cases by prompting specific bits of data. 

Is ChatGPT an LLM?

Yes, ChatGPT is a large language model developed by OpenAI. As a generative pre-trained transformer (GPT), it harnesses an extensive data set comprising text and code. This powerful model identifies patterns and relationships within language, enabling it to produce text that mirrors human conversation, translate across languages, draft creative content, and provide answers to questions [4].

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 organisations to ensure employees have the right level of AI literacy based on their roles and backgrounds [5]. Still, there are potential security and privacy concerns for anyone using the technology, especially when using and creating generated content or confidential company information. Let’s examine the advantages of LLMs and their implementation challenges. 

Advantages of LLMs

With their ability to generate and simulate text similar to that of human language, LLMs contain a specific set of advantages:

• They can easily be customized or fine-tuned to solve specific problems. 

• In conjunction with specificity, LLMs have general characteristics that make them uniquely qualified to solve a range of problems with just one algorithm.

• LLMs grow in accuracy when trained on more parameters and data.

Limitations of LLMs

While some aspects of LLMs seem infinite, limitations in their ability to function exist. Let’s explore some limitations within LLMs:

• Data centers that house LLMs require massive amounts of resources like energy and water, creating environmental challenges for surrounding communities.

• LLMs extract tons of information from the internet, including potential personal information, leading to privacy concerns involving the use of data captured and fed into the model. 

• LLMs create ethical problems around who is responsible for inaccurate or hateful responses.

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

• Since Western society dominates in the production of LLMs, they contain implicit biases and potentially reinforce existing social inequalities. 

How to get started with large language models

You can start interacting with large language models like ChatGPT from OpenAI or Google Gemini to learn how they interact with you. Each chatbot interacts differently. ChatGPT tries to function like a regular conversation by guessing answers to the question without asking for more information. However, Google Gemini focuses on search prompts, giving lists of answers and why it gave them in relation to your initial question, getting more focused on each question. 

Many companies provide a baseline LLM architecture with a framework already in place to create a fine-tuned, customizable agent for your organization. When building an LLM, you can use retrieval augmented generation (RAG) to turn your information into a vector database that the LLM pulls from to create responses. A problematic factor in creating an LLM is the number of parameters, which is why many companies use existing frameworks that use their own data as well as the model's training. 

Learn more with Coursera

If you want to learn more about large language models and AI, start by familiarizing yourself with common AI terms via our Artificial Intelligence Glossary.

Whether you want to develop your AI skills, get comfortable with an in-demand technology, or advance your abilities, keep growing with a Coursera Plus subscription. You’ll get access to over 10,000 flexible courses. 

Build job-ready skills with Coursera Plus

Start 7-day free trial

• 
• 
• 
• 

Start 7-day free trial