What Is a Large Language Model (LLM)?

What Is a Large Language Model (LLM)?

A Large Language Model is a type of AI that has been trained on human language and uses that to generate human-like language for input given by a user. The model learns to recognize patterns in language during the training phase, and then uses those to generate responses to questions posed to it.

When referring to an LLM, “Large” can mean two things. The model has been trained on a huge amount of text data (hundreds of millions of words or more) and it has a large number of parameters (which are the model’s internal “sliding levers” that get tuned during training).

An LLM does not store text and then retrieve and present a text in response to a question. Rather, during training, an LLM learns to recognize statistical regularities within language. The model uses this knowledge to predict (or complete) a portion of text given a prompt.

LLMs can be very powerful for a range of tasks that we complete using language including answering questions, reading and summarizing content, translating between languages, and even generating new content and programming in languages such as Python. These models are becoming the foundation for much of the generative AI on the market today because they provide the language capabilities for many AI-powered assistants and applications.

How Does a Large Language Model Work?

An LLM typically goes through several stages before it can respond to users. The training data consists of vast amounts of text such as books, websites, articles, documents, etc. The amount and kind of text in the training data largely determines the features that the model is going to be good at. The input text is split into the individual tokens (words, parts of words, punctuation marks etc.) which then can be processed by the LLM.

In the training process, the model goes through the tokens in order, tries to predict the next ones, and then fine-tunes its parameters based on the error of its predictions. This process is repeated until the model has gone through the entire training set.

These patterns in language can be used to better answer future questions after the model has gone into production and been trained on the examples provided by users.

This means that when you input a prompt into the model, it processes the input, then generates one token at a time, choosing from all possible tokens of the model given the context of the input. This is why similar questions can receive very different answers from an LLM.

This is why an LLM can return different answers to apparently identical questions. It is generating answers by reference to the patterns it has learned when it was trained on the relevant material.

What Is a Transformer in an LLM?

Modern LLMs are typically implemented as Neural Networks of the so-called transformer architecture. This architecture was one of the key changes in the evolution of large language models, because it enables the model to handle long text well and look up relationships between words in the input sequence. In transformer models, there is an important mechanism called attention, which is used by the model to determine whether a word in the input is relevant to a word it is processing.

For example, a model can decide that in the sentence “I would like a coffee”, the word “like” is less important than the word “would”.

The key to modern LLMs is the transformer architecture. The main reason for their success in dealing with very long input texts is this.

What Are Tokens and Parameters in an LLM?

When we speak of Large Language Models, we typically hear about two things: tokens and parameters.

Here we mention tokens, and describe what they are: how text is split into smaller parts to be processed by the model (i.e. tokens), depending on the language, and on the settings of the tokenizer used.

The parameters of a model are the values inside the neural network which get adjusted during training. Those parameters allow the model to represent the patterns it learned from the training data.

Billions of parameters do not automatically result in the best performance on all tasks. Other factors such as the model architecture, the training data, the training methods, and the model’s capacity to handle context also play a large role.

How Are Large Language Models Trained?

Training an LLM involves large amounts of data, significant amounts of computing power and lots of development to fine tune the model. In the first phase, the model is trained on large quantities of text and learns to reproduce general language patterns, thus it is called pretraining. After pretraining, models can go through additional training steps that are specific to a certain application, for example the tuning of the model to follow a specific set of instructions. This instruction tuning step also includes interactions with human feedback.

After, the model can be further fine-tuned for specific use cases, for example by tuning it for answering questions on the basis of human feedback.

Large language models are typically trained in different ways depending on the individual developer or organization.

Where Are Large Language Models Used?

The wide array of applications that LLMs can implement in relation to text and language in many industries are well documented.

Content Generation

LLMs can generate a wide range of content, including: drafts and complete written pieces, descriptions, summaries, emails and much more. Additionally, the LLM can adapt to a writer’s style and complete work in the writer’s tone and language.

In terms of content output, LLMs are able to change style, word count and even audience whilst still meeting the core remit of the human given instructions. Importantly, however, such generated content still requires human verification to assess for adequate accuracy and subject matter expertise.

Question Answering

An LLM responds to a question by using all the patterns and information it has been trained on.

Additionally, a model’s built-in knowledge does not have to be current or even specific to an organization, as it can easily be connected to knowledge bases or other external sources of information.

Summarization

LLMs can summarize long documents, for example big reports, speeches, conversations etc. This can be very helpful for human beings to get an overview of big amounts of text.

The summaries can contain key points, action items, and even themes from the original content.

Translation

They can translate text between languages, and also adapt the same wording to different contexts.

Translation quality is also language-specific and often can be suboptimal for some languages. Human review of specialized terminology is still required.

Code Generation

All types of programming languages are supported by LLMs, just like natural languages.

Programmers can use LLMs to help write code as well as explain code that has already been written, discover bugs, generate comments and documentation for software, and more.

Always review generated code and test it in a development environment to ensure that it works as expected.

Customer Service

LLMs can also support the building of conversational AI systems to interact with customers over text or voice, answering their questions and summarizing previous conversations and interactions with the customer.

Answer frequently asked questions, summarise previous conversations, classify new conversations, and start to create first draft responses to customer queries. Also, integrated with company systems, the AI-powered assistant is able to retrieve information to help write responses.

Document and Knowledge Search

LLMs are also useful in conjunction with information retrieval systems. Users can search within large collections of documents (e.g. for policies, technical documents, product information, internal knowledge bases etc.).

Search within large collections of documents. This is especially important for large enterprises where employees work within big corporations and search for policies, technical information, product information, and internal knowledge bases.

What Are the Benefits of Large Language Models?

These models can be used to perform a wide variety of tasks, so it is one reason why they have gained such popularity recently. Single Model Handling Many Language Tasks. Large Language Models are powerful and one model can handle many traditional language tasks. To name a few, answering questions, summarizing documents, generating content, coding and more. LLMs can also make natural-language interaction easier. In addition to answering questions, summarizing text and generating content, the models can also process user input in the form of free natural language as input for tasks such as coding and business process automation.

Another benefit to using LLMs is that they are able to handle unstructured information in the form of documents, emails, and even transcripts. This information is very valuable to a business, and LLM-based systems can process and organize it in meaningful ways.

LLMs can also be used to support employees performing repetitive language-based tasks, such as writing, summarizing, classifying, and information extraction.

How much benefit an LLM system brings to a business, however, depends very much on the application and how well the system has been set up to minimize the effort spent by the user to get the results that the user needs.

What Are the Limitations of Large Language Models?

These powerful language systems are far from being able to read and fully understand information in the same manner as a human being. The greatest risk of using an LLM is hallucination. An LLM can create information that looks highly accurate yet is entirely incorrect. This can occur when an LLM provides confident and highly detailed responses to questions. A major limitation to the ways in which LLMs process information is their knowledge boundary. That is, how the LLM has been connected to information after it has been trained up to that point.

LLMs will often reflect any biases found in their training data, which can be addressed through a number of different techniques, but will not necessarily be completely removed.

This kind of model can be expensive to train and require a lot of computing power to run on top of other applications.

When considering the use of LLMs, businesses must also take into account issues of privacy and security. Organizations must be able to understand what information is being sent to an LLM, where the information is being processed, and who has access to sensitive information.

How Are LLMs Used in Business?

Businesses are using LLMs in different ways, depending on their data, processes, and goals. Business applications of LLMs include summarizing customer interactions, searching through company documents, generating reports, assisting employees, customer service, and more. More advanced applications of LLMs are integrated with the organization’s enterprise systems and work with approved data in databases, knowledge bases, CRM systems and other relevant applications within the organization.

When you use an LLM in your business, it is incorporated into a larger application. Thus, it is not the LLM alone that accesses your private data. It is the complete application that accesses your approved data.

To retrieve relevant information from a database, a company could use the retrieval-augmented generation (RAG) technique. This retrieves the necessary information and uses it as context for the model when generating a response.

What Is the Future of Large Language Models?

The business use of LLMs is evolving from isolated chat applications to be used as part of more comprehensive business AI systems. In future applications, the language model will be combined with external knowledge, with software tools, with AI agents, with multimodal capabilities, and with the business applications of a company. By becoming the interface to information and to applications, an LLM can also enable a user to interact with processes that previously were not automated in this way.

However, model efficiency still matters greatly to businesses. Simply using the largest LLM is not sufficient. Often, a smaller LLM is sufficient to deliver good results at lower cost and with less computing power required.

The further development of LLMs does not only consist of increasing the number of parameters and thus the size of the model. Important for practical application are also its accuracy, efficiency, the quality of the generated text, its safety and its ability to integrate with other external systems.