What This Research Area Covers
Research on large language models spans architecture design, training methodology, scaling behavior (how capability changes with model and data size), and the emergent capabilities that appear as models grow.
Why It Matters
LLMs underpin nearly every current AI chat assistant, coding tool, and content generation system — understanding how they're built and what limits their capability is foundational to understanding the rest of the field.
Current Research Directions
Active areas include improving training efficiency, extending context windows, reducing hallucination, and better understanding why scaling produces the specific capabilities it does.
Related Pages
Frequently Asked
What's the difference between an LLM and a chatbot?
The LLM is the underlying trained model; a chatbot is a product built around one with an added interface and features — see our What Are AI Models? page.
Where can I read foundational LLM papers?
See our By Company index for labs that have published foundational LLM research, and our Conferences hub for relevant venues like NeurIPS and ACL.
What does 'scaling' mean in this context?
How model capability changes as you increase model size, training data, and compute — a major area of LLM research.
Where can I learn the basics of how LLMs work?
See our How Large Language Models Work page for a plain-language explainer.