Understanding Large Language Models (LLMs)

By Mike Stott• Published on May 1, 2025

Demystifying Large Language Models (LLMs)

Large Language Models (LLMs) like OpenAI's GPT series, Anthropic's Claude, and Google's Gemini have taken the world by storm. But what exactly are they, and how do they work? This post provides a high-level overview.

What is an LLM?

At its core, an LLM is a type of artificial intelligence model specifically designed to understand, generate, and manipulate human language. They are "large" because they are trained on massive amounts of text data – often terabytes of information from books, articles, websites, and other sources. This extensive training allows them to learn intricate patterns, grammar, context, and even some degree of common-sense reasoning.

The underlying architecture for most modern LLMs is the Transformer network, introduced by Google in their 2017 paper "Attention Is All You Need." Transformers use a mechanism called "attention" to weigh the importance of different words in a sequence when processing and generating text, allowing them to handle long-range dependencies in language effectively.

How Do They "Learn"?

LLMs are typically trained using a process called self-supervised learning. During pre-training, the model is given vast amounts of unlabeled text and tasked with predicting missing words or the next word in a sequence. By repeatedly performing this task, the model learns statistical relationships between words and develops an internal representation of language.

After pre-training, many LLMs undergo a fine-tuning phase. This can involve:

Instruction Tuning: Training the model on examples of instructions and desired outputs to make it better at following commands.
Reinforcement Learning from Human Feedback (RLHF): Human reviewers rate the model's outputs, and this feedback is used to further refine the model's behavior, making it more helpful, harmless, and honest.

Key Capabilities:

Text Generation: Creating human-like text for articles, stories, summaries, emails, etc.
Question Answering: Answering questions based on the knowledge learned during training or provided context.
Translation: Translating text between different languages.
Summarization: Condensing long pieces of text into shorter summaries.
Coding: Generating and assisting with computer code. (See our Use Case on Coding)
And much more: Sentiment analysis, classification, dialogue generation, etc.

Limitations:

Despite their impressive abilities, LLMs have limitations:

Hallucinations: They can sometimes generate plausible-sounding but incorrect or nonsensical information.
Bias: They can reflect biases present in their training data.
Knowledge Cutoff: Their knowledge is generally limited to the data they were trained on, which has a cutoff date.
Lack of True Understanding: While they excel at pattern matching, they don't "understand" concepts in the human sense.

LLMs are a rapidly evolving technology with the potential to impact many aspects of our lives. Understanding their capabilities and limitations is key to harnessing their power responsibly.