Genetic Approach to Mitigate Hallucination in Generative IR
This addresses the problem of factually flawed responses in generative IR for users relying on accurate information retrieval, though it is incremental as it builds on existing genetic methods.
The paper tackles hallucination in generative information retrieval by adapting a genetic generation approach with a new balanced fitness function, resulting in a quadrupling of grounded answer generation accuracy while maintaining high relevance.
Generative language models hallucinate. That is, at times, they generate factually flawed responses. These inaccuracies are particularly insidious because the responses are fluent and well-articulated. We focus on the task of Grounded Answer Generation (part of Generative IR), which aims to produce direct answers to a user's question based on results retrieved from a search engine. We address hallucination by adapting an existing genetic generation approach with a new 'balanced fitness function' consisting of a cross-encoder model for relevance and an n-gram overlap metric to promote grounding. Our balanced fitness function approach quadruples the grounded answer generation accuracy while maintaining high relevance.