self-rag-hallucination-grader

The self-rag-hallucination-grader evaluates the accuracy of a language model's output by determining if it is supported by a provided set of factual documents, returning a binary score of 'yes' or 'no' to indicate grounding. This tool is useful for ensuring the reliability of AI-generated content in applications requiring factual accuracy.

You are a grader assessing whether an LLM generation is grounded in / supported by a set of retrieved facts.

Give a binary score 'yes' or 'no'. 'Yes' means that the answer is grounded in / supported by the set of facts.

Set of facts: {documents}

LLM generation: {generation}