What is an LLM? A Beginner's Guide

If you’re anything like us, you’ve likely been hearing the phrase "large language models" (or LLMs for short) tossed around like confetti lately. Thanks to the explosive growth of AI in 2023, LLMs are really having a moment.

But what exactly are these large language models, and how do they work?

In this beginner's guide, we’ll unpack the basics of LLMs in simple terms. We’ll go over how they’re trained, what they can do, key players in the space, and some concerns around potential misuse. By the end, you’ll understand what all the hype around LLMs is about and where they might be headed next. Let’s dive in!

What Are Large Language Models?

First, what does “language model” mean? A language model is an AI system that’s trained on vast amounts of textual data to understand patterns and relationships within human language. This allows the system to complete useful language tasks like predicting the next word in a sentence, summarising a long text, or generating new sentences from scratch.

Large language models take this concept to the extreme. They ingest massive text datasets—often hundreds of billions of words—to build very advanced models of how language works. The “large” refers to both their training data size and the number of parameters in the model architecture.

Thanks to this huge scale, LLMs pick up nuanced details of how we communicate and compose ideas. They develop what some researchers call “common sense” or “world knowledge” purely from statistics based on seeing so many examples. We don’t have to manually code all of human language’s messy complexities—the models figure it out through data alone.

This broad understanding allows LLMs to complete impressively human-like language tasks. They can answer questions, hold dialogues, generate essays, summarise long texts into bullet points, translate between languages, and much more.

It’s difficult to overstate the leap in language AI abilities unlocked by the scale of modern LLMs. They build on previous breakthroughs in machine learning while cranking data volume and model size to 11. Let’s look under the hood to understand why they represent such a paradigm shift.

How Do LLMs Work?

LLMs rely on deep neural networks, which are AI model architectures loosely inspired by the neurons and connections within the human brain. Here’s a simplified explanation of how LLMs work:

1. The model takes in a “training dataset” of example texts it will learn from. This serves as the model’s world experience.

2. The texts get broken down into shorter chunks and numerical representations. For example, each word gets represented as a vector with hundreds of dimensions.

3. These numerical representations get fed into the neural network model architecture in training batches.

4. The deep learning model finds patterns between the word vectors across many text examples. The model tunes its internal parameters through this training process to get better at predicting relationships.

5. Given new text prompts, the trained model can then generate relevant word predictions and complete language tasks, like translation or summarisation.

Modern LLMs contain a transformer architecture which is particularly well-suited for learning complex language patterns. They also benefit from massive computing power and datasets scraped from the public internet.

The result is AI systems with an impressive ability to understand and generate nuanced, human-like text—a major leap forward from previous natural language processing (NLP) approaches.

However, the model is still brittle in some ways, lacking deeper reasoning abilities. Much work remains to realise truly intelligent language AI.

LLMs in the Real World: What Can They Do?

Thanks to their broad language mastery, LLMs enable diverse applications, including:

• Chatbots and virtual assistants like Siri and Alexa, which can understand complex human instructions as opposed to relying on simple commands.

• Creative writing tools that generate original stories, poems, code and more when given a prompt.

• Automated summarisation of documents into concise overviews.

• Sentiment analysis classification of whether a text expresses positive or negative emotion.

• Machine translation to instantly convert text between languages.

• Question answering systems that provide direct answers to natural language questions.

• Grammar correction systems to fix errors and improve writing style.

• Text auto-completion features in messaging apps and search engines that predict the next word as you type.

LLMs still have obvious limitations—they don’t truly understand language and the world the way humans do. But they continue to get shockingly good at manipulating language in useful ways through brute-force statistical learning (and human feedback).

Next we’ll look at some noteworthy LLM examples.

The LLM All Stars

GPT-3

GPT-3 stands for Generative Pre-trained Transformer 3 and was created by AI lab OpenAI in 2020. It pioneered the recent explosion in LLMs with 175 billion model parameters. It’s the magic behind AI chatbot, ChatGPT. OpenAI have since gone on to release GPT-3.5 and GPT-4. These models power the ChatGPT app from OpenAI, as well as applications like chatbots and automated writing tools for other businesses.

LaMDA

Google’s LaMDA (Language Model for Dialogue Applications) is trained on dialogue data for incredibly natural chit-chat abilities. Google uses LaMDA internally but has not released it publicly.

Claude

Developed by Anthropic—a group of ex-Open AI employees—the Claude LLM (and its latest Claude 2 release) was designed to address safety concerns around autonomy and control over AI’s developing independence. Claude 2 can also work with documents 20x longer than its counterparts.

Llama

A family of open-source LLMs released by Meta in early 2023. Open-source LLMs provide makers with a way to build locally without paying for licensing fees. More recently released Llama 2 is inching closer to proprietary LLMs in performance while allowing commercial use.

PaLM

The Pathways Language Model was developed by GoogleBrain and contains an astounding 540 billion parameters. PaLM 2, its successor, was released in May 2023 and was trained on a lesser count of 340B parameters, but surpassed PaLM on most benchmarks.

This list just scratches the surface of active LLM research and applications across companies and academic labs worldwide. Each new iteration introduces more advanced architectures, training data, and computational scale.

The Future Potential of LLMs

While LLMs have already demonstrated impressive capabilities, they are not yet at human intelligence levels. Here are some ways they are expected to evolve going forward:

• Even larger models will perform better at complex reasoning and multitask learning. Models may eventually contain trillions of parameters.

• New training techniques like reinforcement learning will improve robustness beyond the limitations of supervised learning on static datasets.

• Training on specialised data will produce highly capable domain-specific LLMs for medicine, law, computer science and more.

• Multimodal models trained on both text and other data like images, audio and video will achieve richer understanding.

• Added memory and context modules will reduce limitations of processing only short text snippets independently.

• Chaining multiple models together could combine strengths like creativity, logical reasoning and empathy for more general intelligence.

• Continued advances in computer hardware will enable larger, faster LLM training and inference.

In the coming years, expect LLMs to become better conversationalists, strategists, creators and innovators as their world knowledge grows. Their abilities may even approach sci-fi AI depicted in books and movies—maybe you’ll have your own Jarvis and Iron Man setup at home, before too long.

Researchers emphasise the critical need for ethics and oversight measures as LLMs gain competence.

Should We Be Worried About Large Language Models?

Not to rain on the parade, but we should remember that with great power comes great responsibility. LLMs can be double-edged swords—excellent tools but risky if misused. They can generate content that seems convincingly realistic and human-written, even if untrue. We need to steer them carefully to avoid pitfalls like spreading misinformation or automating people out of jobs.

Potential risks include:

1. Reinforcing harmful societal biases that exist in the training data.

2. Enabling harassment, abuse, and scams in anonymous chatbot interactions.

3. Exacerbating job automation and unemployment as they replace human roles.

4. Infringing on privacy and copyright through data scraping required for training.

These concerns make clear that while LLMs are technically impressive, their impact depends completely on how they are used. Providing oversight and steering their progress responsibly is crucial.

Researchers from Big Tech, academia and startups are studying best practices around issues like algorithmic bias, transparency, and content filtering. User feedback will help surface and fix flaws. Lawmakers also have a key role in crafting regulations to ensure these powerful AI systems benefit society broadly.

Governing the responsible development of ever-more-capable LLMs will only grow in importance. But if stewarded effectively, they can open up amazing possibilities in education, healthcare, entertainment, and beyond.

Key Takeaways

In this beginner's guide to large language models, you learned that:

1. Large language models are AI trained on vast text data to deeply understand language patterns.

2. They use neural networks to learn relationships between words across examples.

3. LLMs can complete ever-more human-like language tasks through statistical learning alone.

4. They enable applications like chatbots, text generation, translation, search, and more.

5. Ongoing LLM research is rapidly improving abilities like reasoning and context.

6. But there are valid concerns around bias, misinformation, automation, and privacy.

7. Responsible development of LLMs will be critical to maximising societal benefit.