What is a large language model, how does it work, and what can it do?
Large language models (LLMs) have become household names lately, thanks to the fundamental role they’ve played in bringing generative AI to the masses. Without LLMs, we wouldn’t have ChatGPT, Google Gemini, or Microsoft Copilot.
Nowadays, most people interact with LLMs regularly (even if they’re unaware of it). Some experts predict that by 2025, more than 750 million applications will be using LLMs.
While the large language model revolution might seem to have emerged out of nowhere, it’s part of an ongoing journey in the AI landscape. Many companies have spent years implementing LLMs at different levels in their operations, taking advantage of advances in machine learning, neural networks, and more.
Today, I’ll attempt to answer the question “What is a large language model?” in simple terms, showing you how they work, where they came from, and why they’re crucial to the future of AI.
What is a Large Language Model? (in Simple Terms)
Large language models (LLMs) are a type of foundation model in the AI landscape, trained on vast amounts of data. They can recognize and generate text, leveraging Natural Language Processing capabilities, and complete many tasks.
LLMs rely on deep learning and underlying “transformer” technology, which consists of a selection of neural networks. The neural networks feature encoders and decoders with self-attention capabilities, which means the models can extract meaning from data and understand the relationships or connections between words, phrases, and other data points.
In a nutshell, an LLM is a computer program fed enough examples and data to recognize and interpret all kinds of complex data, like human language. Many of the top LLMs you’re probably aware of, like the GPT model, are trained on millions of gigabytes of data from the web.
I’ll explain more about how large language models work in a moment. The most important thing you should know is that LLMs can infer from context. They can generate contextually relevant responses to questions, summarize text, and assist in various other tasks.
Developers can also fine-tune LLMs to make them more effective at the task they’re designed for. For example, the Google Gemini selection of LLMs is trained for different purposes. Gemini Nano is intended for smartphone use, while Gemini Ultra is intended for complex tasks.
The Difference Between LLMs and Generative AI
Generative AI and LLMs are closely linked. “Generative AI” refers to artificial intelligence models capable of generating content, like text, code, images, music, and videos. You’re probably familiar with examples like ChatGPT and Midjourney.
Large Language Models are a type of generative AI trained on text and designed to produce textual output, often mirroring human writing. They specialize in language understanding and text generation.
Examples of Popular Large Language Models
Large Language Models are everywhere these days. The most common example is ChatGPT, the world’s leading generative AI chatbot. Other popular forms of LLM models include:
- Google Gemini: Google’s Gemini models and the Pathways Language Model (PaLM) are examples of large language models used in different enterprise settings. Gemini is the family of multimodal large language models created after PaLM 2.
- GPT: Generative Pre-Trained Transformers (GPTs) created by OpenAI are another example of large language models. These models come in different iterations, ranging from GPT-1 to GPT-4 (at the moment).
- BERT: The Bidirectional Encoder Representations from Transformers model (BERT), was also developed by Google. It’s one of the earliest examples of an LLM used to understand natural language and answer common queries.
Other examples include Claude, the family of LLMs created by Anthropic, and Falcon, the transformer-based models developed by the Technology Innovation Institute.
How Do Large Language Models Work?
While answering “What is a large language model” might be simple enough, explaining how they work is more complex. LLMs operate by leveraging a combination of vast amounts of data, various deep learning techniques, and AI algorithms.
Most LLMs we’re familiar with today are based on a “transformer” architecture, like the generative pre-trained transformer (GPT), behind tools like ChatGPT. These transformers are excellent at handling sequential data, like text.
Unlike some other AI solutions, LLMs consist of various levels of neural networks, each “layer” having its own parameters that users can fine-tune with training. These layers are also enhanced by “attention mechanisms,” which allow the program to dial into parts of data sets.
To break it down a little further, let’s look at the core components involved in creating an LLM and how they work to power the system.
Deep Learning and Neural Networks
On a broad scale, large language models are simply very large deep learning models. Deep learning is a form of machine learning that teaches computers to process data in a way similar to the human brain. These models can essentially train themselves to recognize different categories of data and the connections between them without human intervention.
Deep learning algorithms are “neural networks”, modelled after the human brain. Just like the human brain contains millions of different, connected neurons that work together to process information and learn, deep learning “neural networks” consist of numerous “artificial neurons” or nodes.
The nodes in a neural network connect with each other and are organized into multiple layers, such as an input layer (to accept data), an output layer (to convey information), and multiple “hidden layers.”
In most cases, deep learning algorithms use probability to learn, using vast amounts of data to “predict” the right output for each task. Developers enhance the functionality of deep learning solutions with training and fine-tuning.
Transformer Models
Neural networks come in many forms, and the specific types of neural networks used in LLMs are usually “transformer models.” These models can learn context, which is incredibly important in human language. Transformer models use a mathematical solution called “self-attention” to detect how elements in a sequence relate.
This makes transformer models more effective at understanding context than other models. They can understand how the sentences in a paragraph relate to each other, for instance.
During training LLMs, the transformer models learn to “predict” the next word or data point in a sequence based on their knowledge of the preceding words. They assign “probability scores” to certain words that have been tokenized or broken down into character sequences.
What is a Large Language Model Training Process?
Although large language models can learn without human intervention, training is often necessary to ensure these systems perform accurately. LLMs need to be trained on a massive amount of related data, such as text, images, etc. The LLMs used for ChatGPT were trained on billions of webpages, allowing them to learn semantics, grammar, and conceptual learning.
Once they can access this data, LLMs can generate text and outputs by automatically predicting what should come next in a sequence. However, model performance can be further enhanced using various methods, such as prompt-tuning, fine-tuning, and prompt engineering.
Some LLM developers also use reinforcement learning, with feedback from humans, to help reduce biases and “hallucinations” that can occur in LLM models.
What is a Large Language Model Used For?
LLMs might be specially trained to focus on and understand textual content, but that doesn’t mean they’re not versatile tools. Companies and developers can train an LLM for multiple tasks. LLMs can augment virtual assistants and AI chatbots, like IBM Watson or Microsoft Copilot.
They also excel at content generation, making them valuable tools for automating content creation, summarizing and extracting information from data sets, and more. They can even support language translation, breaking down barriers with contextually relevant translations.
Additionally, almost any complex data set can be used to train an LLM, including programming or coding languages. Because of this, LLMs can write code and functions upon request.
Common LLM Use Cases
Notably, the use cases for large language models are constantly evolving. However, LLMs are already making waves in some areas, particularly in the enterprise.
- Text generation: LLMs can generate all kinds of language, writing emails, blog posts, long-form content, sales pitches, and more. They can even edit or customize the tone, grammar, and style of existing content in seconds.
- Code generation: Because code is text-based, LLMs can also assist developers with building applications, finding bugs in code, and uncovering security problems. They can even translate programming languages into different options.
- Summarization: Large language models frequently summarize long articles, reports, news stories, and corporate documentation into simple snippets. You can even summarize meetings and calls with tools like Microsoft Copilot.
- Language translation: LLMs are frequently used to translate language more accurately and effectively. They can enhance language translation capabilities in various applications because they effectively understand context.
- AI assistants: LLMs make chatbots and AI assistants smarter. They can answer customer queries, perform various tasks, like surface information, and provide detailed guidance to team members. Some can even coach and train teams in real time.
- Sentiment analysis: With natural language processing capabilities, LLMs can analyze text to determine tone and words that indicate what a person is feeling. This makes them excellent at sentiment analysis, particularly in customer service.
The Scope of Large Language Models
LLMs are one of the fastest-growing forms of artificial intelligence today. By 2032, experts predict the LLM market will be worth more than $53.9 billion. Part of the reason for the continued growth of large language models is their versatility.
Large language models can support companies in virtually any industry. In the technology landscape, they can enhance search engines, help developers write code, and assist with creating new programs. For healthcare and science, LLMs can understand proteins, DNA, molecules, and more, which makes them excellent at assisting with the development of vaccines and medicines.
In customer service, large language models are used across industries to support customers on a 24/7 basis, with creative responses to queries. They’re also used for marketing and sales purposes, providing insights into sentiment, or generating personalized customer recommendations.
Even in more regulated industries, like the legal landscape or banking, LLMs can assist experts with generating documents, detecting fraud, and analyzing information.
What is a Large Language Model Good For? The Benefits
There’s no denying that LLMs can offer incredible benefits to the human race. They can support a broad range of applications and can even respond to unpredictable queries, unlike most AI solutions. Some of the most significant benefits of large language models include:
- Versatility: LLMs can be trained to perform various tasks, from translating languages to creating content, analyzing sentiment, completing mathematical equations, and delivering customer service. This makes them suitable for countless applications.
- Evolution: Because LLMs are deep learning models, they’re constantly learning, adapting, and improving based on the data they access. The more data they gather, the more effective they become at the tasks they were designed to complete.
- Performance: A key characteristic of LLMs is their exceptional accuracy in answering unstructured questions and responding to unpredictable input. They learn quickly, adapt rapidly to different scenarios, and can scale over time.
The Problems with Large Language Models
Unfortunately, like most forms of AI, large language models also have their limitations. On a broad scale, LLMs are only as good as the data used to train them. This means they can suffer from various issues caused by incomplete data sets.
AI hallucinations are common in LLMs because AIs essentially predict the most likely response to a query. However, they can still make mistakes if they don’t fully understand what a person is asking for, which is why effective training and prompting are crucial.
Even worse, since the data used to train LLMs will affect the outputs they produce, bias in data can lead to bias in responses. For instance, LLMs used to help Amazon hire employees were found to be biased towards male candidates.
On top of that, large language models can present significant privacy and security risks. They can accidentally leak a person’s private information, produce spam, and even be programmed to participate in phishing scams. Criminals can even break into an LLM and reprogram the application maliciously. Speaking of data, it’s often difficult to know that the vast amounts of information collected to train LLMs have been obtained legally.
Scraping data from the internet can mean that LLMs ignore copyright licenses, plagiarize existing content, and fail to access permission to leverage sensitive information.
At the same time, building, deploying, and scaling a comprehensive large language model can be time-consuming, complex, and expensive. This makes it difficult for organizations to embrace LLMs in their day-to-day processes.
What’s the Future of Large Language Models
Now you know the answer to “What is a large language model?” and how it works, you might be wondering what’s next on the horizon. Interest in LLMs has continued to skyrocket in the last year, and companies are working on making LLMs increasingly more powerful and advanced.
For instance, multimodal large language models are becoming increasingly popular. These LLMs, like Google Gemini, are trained in video and audio input, as well as text. This allows them to complete an even wider range of tasks, and understand more forms of data.
The abilities of LLMs are evolving, too. Newer solutions are more accurate and more effective at avoiding hallucinations and bias and they’re better at understanding different types of human language. As we move into the future, LLMs have the power to transform countless applications, change the way we work, and pave the way for better customer experiences.
