Large Language Model(LLM)

A large language model learns by doing one deceptively simple thing over and over: predicting the next word in a piece of text. Trained on a large share of the public internet, books, and code, it builds up a rich statistical sense of how language fits together.

That single skill, done well enough and at a large enough scale, produces something that looks a lot like understanding. Ask it a question and it generates a plausible answer one word at a time. The “large” is not marketing: these models have billions of internal settings, called parameters, and are trained on volumes of text no person could read in a thousand lifetimes.

They are a type of foundation model, and their great strength is flexibility. The same model can summarise a legal document, explain a recipe, or help debug a program, without being purpose-built for any of them.

Their great weakness is that they have no built-in sense of truth. Because they generate text that is statistically likely rather than verified, they can state something false with complete confidence, a failure the field calls hallucination. That is why so much recent work focuses on grounding these models in reliable sources.

In the UK, the most advanced LLM work is at Google DeepMind in London, whose Gemini models sit at the frontier. But much of the British industry we cover does not train its own: it builds on existing models, fine-tuning them and grounding them in trusted data for law, healthcare and finance — a way to compete without the hundreds of millions a frontier model costs.