What is a Large Language Model (LLM)? Definition, Examples, Use Cases

In the realm of artificial intelligence (AI), Large Language Models (LLMs) are reshaping how we interact with technology and consume information.

These AI systems, driven by deep learning algorithms, have taken the world by storm for their remarkable ability to generate human-like text and perform a wide range of language-related tasks. But what are LLMs, and how do they work?

In this article, we'll define what LLMs are, delve deep into their structure, and give examples of their use cases within the enterprise. 

What is a Large Language Model (LLM)? Definition

A Large Language Model (LLM) is a foundational model designed to understand, interpret and generate text using human language. 

It does this by processing datasets and finding patterns, grammatical structures and even cultural references in the data to generate text in a conversational manner. 

One defining characteristic of LLMs is their scale. Built on numerous layers and millions (or even billions) of parameters, LLMs are trained on massive amounts of data and capture intricate relationships between words to predict the next word in a sentence. 

The systems then use self-supervised learning to process this data over and over until they reach a high level of accuracy and can autonomously complete text prompts, translate languages, and even write content like a human. 

The quality of a language model largely depends heavily on the quality of the data it was trained on. The bigger and more diverse the data used during training, the faster and more accurate the model will be. 

Modern LLMs today are fed on extremely large datasets scraped from the web. Recent advancements in hardware capabilities, paired with improved training techniques and the increased availability of data, have made language models more powerful than ever before. 

How do LLMs Work?

The first step in building a large language model is to determine what type of LLM you want to build. This involves gathering a vast and diverse dataset of text from various sources, such as books, articles, websites, and more. This dataset acts as the foundation for training the model. 

The collected text data is then preprocessed, which involves tasks like tokenisation (breaking text into words or subwords), lowercasing, removing punctuation, and encoding text into numerical code suitable for machine learning. During preprocessing, each token (word or subword) is converted into a vector representation called an embedding. Embeddings capture semantic information about words, allowing the model to understand and learn the relationships between them. 

Large Language Models are typically made up of neural network architectures called transformer architectures. First coined in Google’s paper "Attention Is All You Need", transformer architectures rely on self-attention mechanisms that allow it to capture relationships between words regardless of their positions in the input sequence.

Since the transformer architectures don't consider the order of words in a sequence, positional encodings are needed to provide information about the position of each token in the sequence, enabling the model to understand the sequential structure of the text.

Then comes training the LLM. This involves feeding sequences of tokens into the model and optimising its parameters to minimise the difference between the predicted and actual next tokens. The training process requires significant computational resources, often involving distributed computing and specialized hardware like Graphics Processing Units (GPUs) or even custom hardware like TPUs (Tensor Processing Units).

Training large language models is an iterative process. Models are trained on large datasets for many epochs, gradually improving their performance. After training, fine-tuning may be performed on more specific tasks or domains to adapt the model to particular applications.

Use Cases of LLMs 

Once the training process is complete, the resulting large language model can be used for a wide range of natural language processing tasks.

Here are some use cases of LLMs:

• Content Generation – Language models can automatically generate high-quality content for a variety of purposes, including articles, blog posts, product descriptions, and marketing materials. They can assist content creators by suggesting topics, drafting text, and even adapting writing styles to match specific tones or audiences.
   
• Translation – Translators can use LLMs to streamline the translation process. They can be used to translate text in real time for global communication, content localisation, and international business operations.
   
• Chatbots and Customer Support – LLM-powered chatbots can provide instant and personalised customer support. They can answer questions, generate text and images from user prompts, and troubleshoot user issues. 
   
• Writing Code – Language models can help programmers by generating code snippets, explanations, and documentation based on natural language queries. They aid in coding tasks, debugging, and learning programming concepts.
   
• Medical Diagnostics and Research – Healthcare professionals can use LLMs in medical research to analyse and summarize medical texts. They can also help in diagnosing diseases, predicting outcomes, and identifying potential treatment options.
   
• Education and E-Learning – LLMs power adaptive learning platforms that provide personalized educational content and assessments. They cater to individual learning styles and progress, offering a more tailored and effective learning experience.
   
• Legal and Compliance Documentation – LLMs can assist in drafting legal documents, contracts, and compliance reports by generating accurate and contextually appropriate text based on specific legal requirements.
   
• Data Analytics – LLMs aid in data analysis by generating descriptive reports, data summaries, and insights from complex datasets, assisting businesses in making informed decisions.

Examples of LLMs

As generative AI takes the world by storm in 2023, the majority of emerging AI systems are powered by a number of powerful Large language models that dominate the market. 

Here are some of the most popular examples of LLMs today:

1. GPT– OpenAI’s Generative Pretrained Transformer (GPT) is perhaps the most widely known LLM. It powers the explosive AI chatbot ChatGPT, and. Microsoft also uses GPT-4 to power its Bing Chat platform.
    
2. LaMDA – LaMDA was created by Google and powers Google’s own conversational chatbot, Bard. 
    
3. LLaMA – This is the LLM used by Meta AI. Meta recently released an open-source version of LlaMA, known as LLama 2
    
4. Megatron-Turning NLG – Developed by Nvidia and Microsoft, Megatron-Turning NLG is the largest the most powerful monolithic transformer English language model, boasting 530 billion parameters. 
    
5. Claude – Developed by the AI company Anthropic, Claude is a next-generation LLM that powers Athropic’s conversational chatbot by the same name.