What is a Large Language Model? Uses, Scope, and Limitations

Large Language Models (LLMs) are popular artefacts of modern artificial intelligence. Although LLMs are based on well-established technology, they have become popular recently due to the widespread availability of low-cost, scalable cloud infrastructure.

In this post, we’ll cover what a large language model is in AI, what it can be used for, and its scope.

What is a Large Language Model?

A Large Language Model is an artificial intelligence model trained on huge amounts of textual data to understand and create human-like text. Large Language Models are usually based on the foundation of deep learning techniques, like neural networks, which we will discuss in more detail later.

Examples of Large Language Models include ChatGPT, Google’s Gemini (previously Bard), and Llama.

What Is a Large Language Model Useful For?

As they can respond to unpredictable questions promptly, Large Language Models have many applications, both for businesses and private use.

• Human-written text processing

One use of a Large Language Model is processing human-written text, particularly pattern recognition in human text and generating new text. LLMs can analyse text and give information about the sentiment that text conveys. They can summarise larger blocks of text into smaller blocks of text. One of the most popular uses of an LLM (like ChatGPT or Copilot) is to create an interactive interface where users can ask questions.

• Text generation

Large Language Models are also commonly utilised for text generation. These models can create human-like text based on human prompts, making them useful for writing assistance. 

• Language translation

You can also use Large Language Models for language translation, as they can translate content between different languages with greater precision than many other tools.

• Content recommendation

Large Language Models can scrutinise user preferences and behaviours to recommend personalised products, content, and services, which can boost user engagement and experience.

• Sentiment analysis

You can use Large Language Models to analyse the sentiment of text. This can help your business understand consumer feedback and sentiment on social media, which can effectively shape your next strategy.

The limitations of using Large Language Models

Large Language Model uses can help your business excel, but they don’t come without limitations. Let’s take a look at some considerations of using LLMs below.

• Relies on correct data to give correct answers

LLMs are not good at computation, numeric analysis, or predicting sales. They cannot even do simple addition (without supplementary enhancements). When an LLM solves a mathematical problem, it does so based on the previous text it was trained with and not by really solving the problem. It would give an incorrect answer if trained on incorrect data.

In essence, a Large Language Model replies to a query by simulating what a human may write next. This is worth considering if you plan on using a Large Language Model in your business.

• Potential copyright infringement

LLMs are typically trained on massive text data sets. Early attempts at modern LLMs were trained on data that was easily accessible on the public Internet. More recent LLMs are trained on a much wider scope of written material. This has given rise to copyright concerns and questions about whether the data used to train LLMs was authorised for this purpose.

• Cannot be altered once trained

As neural network training is a long, expensive process, an LLM cannot be significantly altered or trained further once trained. When one uses ChatGPT, for example, it (mostly) does not learn anything new from the conversation. Once the conversation is finished, it is forgotten about! For example, try asking ChatGPT (model GBT-4 Turbo) who won the Super Bowl in 2024. This LLM was trained before the Super Bowl, so it cannot answer as it has no knowledge of these events.

Therefore, LLMs need further enhancement (for example, using Retrieval Augmented Generation) before they are useful for specific use cases. For example, McKenna Consultants uses Retrieval Augmented Generation to produce our Assistant AIs for our clients.

Using Large Language Models: how do they work?

An LLM is a purpose-specific example of a neural network. The first computerised neural network was created way back in 1957! Neural networks seek to solve complex problems that are otherwise hard to solve with computers by simulating the function of a human brain. This simulation is primitive but effective in certain circumstances.

A neural network contains “neurons” which you can think of as tiny individual equations that perform some calculation on a given input. The neurons are chained together into a large network so that the output of one set of neurons is the input into more neurons.

A neural network is “trained” with a large (the “Large” from Large Language Model) quantity of data. The neural network begins life not functioning well. A well-known set of data is inputted into the neural network and the neural network acts as a “magic black box” which spits out an answer. In the beginning, the answer will be wildly incorrect.

The next step is to adjust semi-randomly the internal workings of the neural network and repeat the test. If the result is better, the neural network is corrected more in the same way. If the result is worse, it is corrected differently.

Training a neural network is a very slow, painstaking process that requires a large amount of computing power, input data, and test data.

Make the most of the Large Language Model uses

If you think using Large Language Models could benefit your business, please get in touch with us at McKenna Consultants. We are well-experienced in the field of artificial intelligence, from using AI to improve customer experience to creating an AI assistant with Large Language Models. You can find more information on artificial intelligence, take a look at our blog.