Genetic Generative Information Retrieval

Kulkarni, H., Young, Z., Goharian, N., Frieder, O. and MacAvaney, S. (2023) Genetic Generative Information Retrieval. In: 23rd ACM Symposium on Document Engineering (DocEng'23), Limerick, Ireland, 22-25 Aug 2023, ISBN 9798400700279 (doi: 10.1145/3573128.3609340)

Abstract

Documents come in all shapes and sizes and are created by many different means, including now-a-days, generative language models. We demonstrate that a simple genetic algorithm can improve generative information retrieval by using a document's text as a genetic representation, a relevance model as a fitness function, and a large language model as a genetic operator that introduces diversity through random changes to the text to produce new documents. By "mutating" highly-relevant documents and "crossing over" content between documents, we produce new documents of greater relevance to a user's information need --- validated in terms of estimated relevance scores from various models and via a preliminary human evaluation. We also identify challenges that demand further study.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: MacAvaney, Dr Sean
• Authors: Kulkarni, H., Young, Z., Goharian, N., Frieder, O., and MacAvaney, S.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9798400700279
• Copyright Holders: Copyright © 2023 ACM
• First Published: First published in Proceedings of the 23rd ACM Symposium on Document Engineering 2023
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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