ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval
CoRR(2024)
摘要
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
are fully open-sourced at .
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