Genetic Approach to Mitigate Hallucination in Generative IR

Hrishikesh Kulkarni, Nazli Goharian, Ophir Frieder, Sean MacAvaney

Keywords: Generative IR, Hallucination, Genetic Algorithm

TL;DR: We propose 'Genetic Approach using Grounded Evolution' (GAuGE) to mitigate hallucination while maintaining high relevance to user query in generative language models.

Abstract: 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.

Submission Number: 9