What Are Large Language Models?

Large language models (LLMs) are a type of artificial intelligence (AI) that uses machine learning algorithms to replicate human language. It uses massive data sets to develop its 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 it 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. 

Read more: What Is Natural Language Generation?

What are large language models used for?

From generating content to creating the foundations for AI chatbots, LLMs have a range of uses.

• Generating content: LLMs rewrite, summarize, and generate new text based on a prompt or topic it is familiar with. 

• Translation: With proper training, LLMs can translate between languages.

• Chatbots: LLMs power chatbots like ChatGPT-4, Google PaLM, and Meta’s LLaMA, all of which interact with users in a familiar dialogue style.

• Categorizing text: LLMs classify text and organize it into specific categories. 

LLMs have the power to perform any number of tasks related to the use of language and can even automate everyday language tasks. 

Read more: What Is Prompt Engineering? Definition and Examples

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. 

Who uses large language models?

Various industries use LLMs to create unique customer experiences with chatbots, support scientific research in classification, and easily create meeting transcripts. LLMs can also help marketing teams organize customer feedback and see how their audience talks about their brand through sentiment analysis. 

Some specific jobs in data science train, develop, and use LLMs in their work, such as:

• Reinforcement learning research scientists

• Machine learning engineers

• Deep learning scientists

Let’s look closer at each job, their salary, and how they interact with LLMs. 

Read more: What Is Deep Learning? Definition, Examples, and Careers

1. Reinforcement learning researcher

Average annual salary: $110,365 [1] 

Reinforcement learning (RL), sometimes called reinforcement learning from human preferences (RLHF), is a machine learning feedback mechanism that involves human input in the algorithm training process. This can improve the language abilities of LLMs with complex human emotions or associations with language. 

2. Natural language processing engineer

Average annual salary (US): $100,100 [2]

Natural language processing (NLP) is the basis for using LLMs. The training of LLMs produces NLP tasks like translation, chatbots, and human language production. An NLP engineer must understand the linguistic properties of human language and how to create machine-learning algorithms to replicate them. 

3. Deep learning scientists

Average annual salary (US): $132,663 [3]

Deep learning allows for more complex uses for natural language processing, creating LLMs that replicate human speech in uses like chatbots. Deep learning algorithms allow for the recognition of text meaning and have the ability to replicate it similarly to human language. 

Advantages and challenges of large language models

LLMs come with advantages and challenges when assessing their use in society. Currently, no laws govern the use of LLMs, which creates potential security and privacy concerns for the technology, especially around the use and creation of generated content. 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 Bard 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 Bard 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) as a way 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.

On Coursera, you can try the Generative AI with Large Language Models course from AWS and DeepLearning.AI to gain the fundamentals of creating LLMs for generating AI models. Alternatively, you can enhance your understanding of deep learning with the Deep Learning Specialization from DeepLearning.AI. Upon completing either program, gain a shareable Professional Certificate to include in your resume, CV, or LinkedIn profile.