ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval
Soyoung Yoon, Eunbi Choi, Jiyeon Kim, Hyeongu Yun, Yireun Kim, Seung-won Hwang
Abstract
We propose ListT5, a novel reranking approach based on Fusion-in-Decoder (FiD) that handles multiple candidate passages at both train and inference time. We also introduce an efficient inference framework for listwise ranking based on m-ary tournament sort with output caching. We evaluate and compare our model on the BEIR benchmark for zero-shot retrieval task, demonstrating that ListT5 (1) outperforms the state-of-the-art RankT5 baseline with a notable +1.3 gain in the average NDCG@10 score, (2) has an efficiency comparable to pointwise ranking models and surpasses the efficiency of previous listwise ranking models, and (3) overcomes the lost-in-the-middle problem of previous listwise rerankers. Our code, model checkpoints, and the evaluation framework will be fully open-sourced.
BibTeX
@inproceedings{yoon-etal-2024-listt5,
title = "{L}ist{T}5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval",
author = "Yoon, Soyoung and
Choi, Eunbi and
Kim, Jiyeon and
Yun, Hyeongu and
Kim, Yireun and
Hwang, Seung-won",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.125/",
doi = "10.18653/v1/2024.acl-long.125",
pages = "2287--2308"
}