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.
Cite
@article{arxiv.2409.00085,
title = {Genetic Approach to Mitigate Hallucination in Generative IR},
author = {Hrishikesh Kulkarni and Nazli Goharian and Ophir Frieder and Sean MacAvaney},
journal= {arXiv preprint arXiv:2409.00085},
year = {2024}
}