Genetic Generative Information Retrieval

link bibtex code slides 2 citations short conference paper

Authors: Hrishikesh Kulkarni, Zachary Young, Nazli Goharian, Ophir Frieder, Sean MacAvaney

Appeared in: The 23rd ACM Symposium on Document Engineering (DocEng 2023)

Abstract:

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.

BibTeX @inproceedings{kulkarni:doceng2023-gen2ir, author = {Kulkarni, Hrishikesh and Young, Zachary and Goharian, Nazli and Frieder, Ophir and MacAvaney, Sean}, title = {Genetic Generative Information Retrieval}, booktitle = {The 23rd ACM Symposium on Document Engineering}, year = {2023}, doi = {10.1145/3573128.3609340} }