Hallucinations

Reduce LLM Hallucinations with Grounding and Verification

Large language models can produce fluent answers that contain incorrect facts, invented details, or unsupported claims. This behavior is commonly called hallucination. Hallucinations are not simply random mistakes. A language model generates tokens that are plausible given its input and learned parameters. Plausible text is not necessarily true text, especially when the model lacks reliable evidence for the question being asked. For developers, the practical goal is therefore not to find a single setting that eliminates hallucinations. It is to design the application so that factual claims are grounded in appropriate evidence, uncertainty is handled explicitly, and important outputs are verified.