Genetic Approach to Mitigate Hallucination in Generative IR
<|reference_start|>Genetic Approach to Mitigate Hallucination in Generative IR: 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.<|reference_end|>
@article{kulkarni2024genetic,
    title={Genetic Approach to Mitigate Hallucination in Generative IR},
    author={Hrishikesh Kulkarni, Nazli Goharian, Ophir Frieder, Sean MacAvaney},
    journal={The Second Workshop on Generative Information Retrieval at ACM
  SIGIR 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00085},
    primaryClass={cs.CL cs.IR}
}