What is a large language model (LLM) in AI?

Recently, AI technology has hit the mainstream, with many people getting exposed to technical terms like LLM, GPT, neural network, and more. However, these terms don’t explain much if you’re unfamiliar with the basics of the underlying technology. I believe it’s crucial to understand how these emerging AI technologies work so you know what to expect in the future. After all, the tools are already disrupting many industries in unprecedented ways.

In this article, I’ll explain everything there is to know about LLMs or large language models. This AI technology is behind popular tools like ChatGPT, Gemini, Grok, and more, meaning you should know how they work if you want to utilize them effectively. So, let’s dive into the exciting world of large language models.

What does LLM stand for, and what is a large language model?

LLM stands for large language model, a type of machine learning model trained on vast amounts of text data. It can also be described as a computer program that interprets text data and gives an output based on a user’s input, typically called a prompt.

As the name suggests, LLMs are language models, meaning they can do various text-related tasks, such as creating new content, translation, interpretation, editing, and more. You can apply such an AI program in multiple ways. ChatGPT is the most obvious example of a robust LLM in action. However, this service is also a large multi-modal model capable of generating text, images, video, and even audio based on a user's prompt.

The emergence of transformer architecture

The biggest leap for LLMs happened in 2017 with the emergence of transformer neural networks, which were described in the "Attention Is All You Need" research paper. Basically, it’s a deep learning architecture that significantly improves neural networks' learning time. This led to the development of GPTs (generative pre-trained transformers), which are all the rage now.

The key breakthrough was parallelization, allowing multiple tasks to be done simultaneously rather than in sequential order. This enabled modern LLMs to utilize powerful GPUs to train larger models more efficiently on vast troves of data.

Before that, LLMs relied on recurrent neural networks (RNNs), which were less efficient due to their sequential workflow. Because of this, the AI systems available to the public weren’t as powerful since they couldn’t iterate as quickly as they do now.

Mechanisms behind LLMs

Large language models are trained on massive data sets scraped from the internet and various other archives. The programs utilize deep learning, a type of machine learning algorithm, to learn how to recognize patterns in various data. Usually, this is done following a self-supervised learning (SSL) paradigm, where the program creates training signals based on the input data.

Later, the program can be fine-tuned in various ways to improve its output. Typically, the fine-tuning process is done in a supervised manner via reinforcement learning from human feedback. In short, it means supervisors analyze an AI agent’s output and then adjust its parameters so its future results align with the creator’s goals. During this time, the supervisors also train the agent on new data to further improve its performance. Additionally, you can fine-tune the entire AI algorithm or a subset of it.

Training LLMs on high-quality human-generated data is crucial to ensure accuracy and relevancy. Nowadays, simply scraping the web for training data isn’t ideal since some of the content on the internet is AI generated. Training an agent on AI content leads to a deterioration in quality since the initial data isn’t based on anything real.

An example of a typical data set used to train AI models would be The Pile – an open-source 800GB compilation of various English texts curated specifically for LLMs. Another option is Common Crawl, which is an open repository of data scraped from the internet that’s freely available to use for everyone. Training an AI model on loads of data makes it more capable at performing various tasks.

Real-world examples and use cases

ChatGPT is the most prominent example of the capabilities of large language models. It is an incredibly versatile chatbot that can perform an impressive array of tasks due to its multimodal nature. Besides text, the service can generate images, graphs, analyze files, create programs, audio, and more. However, since the service operates on a freemium model, many of its most powerful features are only available to paying customers.

The popularity of ChatGPT quickly nudged other massive tech companies to create their own AI products. For example, Meta has Llama, X (Twitter) has Grok, Google has Gemini, Microsoft has Copilot, and so on. Each agent is integrated into the companies’ products to improve customer efficiency or to enable new features entirely. For example, you can use Gemini to make suggestions or summarize a document using Google Docs.

On a smaller scale, companies can use LLMs trained on internal data to create customer support chatbots. This approach can ease the pressure on your customer support specialists, allowing them to focus on more important tasks while the chatbot answers basic customer questions. The human agents can intervene whenever the AI cannot resolve a customer’s issue.

Differentiating LLMs from other AI models

Although LLMs are all the rage at the moment, they’re not the only AI model on the market. After all, large language models focus solely on language-related tasks like translation, summarizing, and creation. However, other AI products like Stable Diffusion and Dall-E perform different functions because they’re based on different AI models. So, let’s quickly overview the key AI models besides LLMs.

• Generative models. The previously mentioned models are examples of AI models for generating images. You can use generative models to create audio and video, too. As expected, these models are trained on vast amounts of data with explanatory labels.
• Reinforcement learning. These models evolve by interacting with a particular environment. They learn by receiving rewards and penalties for their actions. Such AI models are used to train robots and game systems like AlphaGo.
• Computer vision. As the name suggests, these programs are geared towards image recognition, object detection, and other visual data tasks. This AI model is behind all the apps like YOLO (You Only Look Once), which can recognize objects from photographs.
• Tabular. These AI models work with structured data (spreadsheets, databases) for classification and prediction purposes. You can use these tools to quickly analyze vast amounts of data and make predictions.

Limitations of large language models

LLMs are definitely impressive, but they also have some limitations and even occasional issues. For example, depending on your prompt, the output might not necessarily be accurate or factually correct. The problems can occur due to a vague prompt or lack of information in the AI’s training data.

At best, the service will notify you that it lacks sufficient data to respond accurately. However, it may also hallucinate and create fictitious information in its reply. That’s why it’s crucial to always double-check and verify the agent’s output. At the very least, don’t use LLMs as a replacement for legal counsel since they’ll come up with fake cases to support their position.

Secondly, the bias in its training data will influence an LLM's responses. This could lead to the AI favoring specific ideas over others, thus limiting its potential. The biased responses can also offend customers, which was exactly the case with ChatGPT. It was accused of favoring left-leaning ideals over other perspectives, thus forcing OpenAI to adjust the chatbot sometimes to give responses the company doesn’t agree with.

Finally, large language models aren’t built with security and privacy in mind. Whatever information you share with it becomes a part of its training data, meaning someone else could extract it. This includes various personal details you share with it, as well as work-related documents you give the AI to overview. There was even an incident where ChatGPT leaked user credit card details. So, think twice before using ChatGPT to fill in the blanks in your everyday work, as you may unintentionally leak vital business information.

Ethical considerations and future directions

I think everyone should consider the ethical implications of using LLMs and their impact on our lives. After all, while these technologies are groundbreaking, they also come with some unfortunate consequences.

• Easy to create convincing disinformation. AI technology is used to easily create deepfakes of real people in various situations. This has many potential applications, such as creating crypto scams with Elon Musk or spreading misinformation by conducting mass robocalls with an AI-generated voice of Joe Biden.
• Copyright infringement issues. Some companies scraped the internet for data without considering whether netizens consent to their creations being used to train AI. This means some AI systems were trained on copyrighted material and can even generate new content that’s based on someone else’s intellectual property.
• Job market disruption. As a writer, I’m concerned about how AI will impact my work. At best, it can assist me during the research process by summarizing complex topics. However, it also means there will likely be fewer prospects in the future since companies can use LLMs to quickly generate text without effort. Naturally, this job market disruption affects not just me but also graphic designers, artists, musicians, and other professionals beyond the creative industries.
• Environmental impact. AI systems require massive computational power to operate, which doesn’t bode well for the environment. The mass adoption of LLMs means more data centers, the need for more powerful hardware, and more power consumption overall, which leads to more emissions in the long run.

How can beginners start learning about LLMs?

Nowadays, it’s easier than ever to learn about LLMs and AI. Their prevalence in the current market means everyone will benefit from having a basic understanding of these emerging technologies. However, note that AI is a very complex topic that requires years of study to understand fully.

The first step is to try out these systems yourself from an end-user perspective. ChatGPT started the whole LLM boom and is available for free, allowing you to test its capabilities without commitment. After that, you can peruse the OpenAI news section to learn about the company’s research and new developments with its chatbot. This approach also applies to other LLMs from large tech companies. I think the Google AI blog is also a great option since the company greatly impacted the development of transformers.

For independent learning, Coursera is a good place to start. The platform offers loads of free courses about various computer science topics, including generative AI with large language models. However, you should look for courses related to artificial intelligence if you want to learn the fundamentals behind LLMs. YouTube channels like Two Minute Papers and DeepLearningAI are also excellent choices.

ArXiv is the place to be if you want to learn about cutting-edge AI research development. Here, you can read the foundational Attention is All You Need paper that kick-started the GPT breakthrough. Remember that these are academic papers that might be hard to read.

Conclusion

LLMs and AI are exciting concepts that are significantly shaking up the tech industry and other areas of life. Even if your day-to-day life isn’t affected by this technology at the moment, it’s pretty likely that it will have some kind of impact in the near future. So, I suggest bracing yourself for the coming change by learning more about large language models and how to utilize them. It’s a complicated topic for sure, but that doesn’t mean you should stay ignorant about a topic affecting the world around you.