@inproceedings{Kulkarni2023GeneticIR,
author = {Kulkarni, Hrishikesh and Young, Zachary and Goharian, Nazli and Frieder, Ophir and MacAvaney, Sean},
title = {Genetic Generative Information Retrieval},
year = {2023},
isbn = {9798400700279},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3573128.3609340},
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.},
booktitle = {Proceedings of the ACM Symposium on Document Engineering 2023},
articleno = {8},
numpages = {4},
keywords = {genetic algorithm, generative information retrieval, large language models},
location = {Limerick, Ireland},
series = {DocEng '23}
}