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    <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.</abstract>
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%0 Conference Proceedings
%T ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval
%A Yoon, Soyoung
%A Choi, Eunbi
%A Kim, Jiyeon
%A Yun, Hyeongu
%A Kim, Yireun
%A Hwang, Seung-won
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F yoon-etal-2024-listt5
%X 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.
%R 10.18653/v1/2024.acl-long.125
%U https://aclanthology.org/2024.acl-long.125
%U https://doi.org/10.18653/v1/2024.acl-long.125
%P 2287-2308

Markdown (Informal)

[ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval](https://aclanthology.org/2024.acl-long.125) (Yoon et al., ACL 2024)

• ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval (Yoon et al., ACL 2024)

ACL

• Soyoung Yoon, Eunbi Choi, Jiyeon Kim, Hyeongu Yun, Yireun Kim, and Seung-won Hwang. 2024. ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2287–2308, Bangkok, Thailand. Association for Computational Linguistics.