In-Context Learning (ICL) is a capability of Large Language Models (LLMs) to learn and perform tasks based solely on the context provided within a Prompt, without updating the model's parameters. By presenting examples or instructions within the input, LLMs can adapt to new tasks temporarily, enabling few-shot learning where the model generalizes from limited data. This emergent property of LLMs contrasts with traditional training methods that require parameter adjustments and extensive datasets.
The LLM Knowledge Base is a collection of bite-sized explanations for commonly used terms and abbreviations related to Large Language Models and Generative AI.
It's an educational resource that helps you stay up-to-date with the latest developments in AI research and its applications.