LLM (Large Language Model)

Executive Definition: LLM (Large Language Model) — A deep learning model trained on vast amounts of text data, designed to understand, generate, and interact with natural language.

Large Language Models are built using the transformer architecture and contain billions or even trillions of parameters. By analyzing statistical patterns across extensive training data, they acquire the ability to perform a wide variety of tasks, including translation, summarization, coding, and open-ended conversation, without needing task-specific retraining.

Frequently Asked Questions

How are LLMs trained?

LLMs are first pre-trained on massive text corpora to predict the next word. They are then fine-tuned for instruction following and aligned using techniques like RLHF (Reinforcement Learning from Human Feedback) to produce safe and helpful responses.

What are the limitations of LLMs?

LLMs can hallucinate (generate false information confidently), struggle with complex mathematical or logical reasoning without external tools, and suffer from a limited context window when processing very large documents.

What is the context window in an LLM?

The context window is the maximum amount of text (measured in tokens) the model can process at one time. If a conversation or document exceeds this limit, the model will ‘forget’ earlier information.