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        <title>Joint Inference of Retrieval and Generation for Passage Re-ranking</title>
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    <abstract>Passage retrieval is a crucial component of modern open-domain question answering (QA) systems, providing information for downstream QA components to generate accurate and transparent answers. In this study we focus on passage re-ranking, proposing a simple yet effective method, Joint Passage Re-ranking (JPR), that optimizes the mutual information between query and passage distributions, integrating both cross-encoders and generative models in the re-ranking process. Experimental results demonstrate that JPR outperforms conventional re-rankers and language model scorers in both open-domain QA retrieval settings and diverse retrieval benchmarks under zero-shot settings.</abstract>
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%0 Conference Proceedings
%T Joint Inference of Retrieval and Generation for Passage Re-ranking
%A Fang, Wei
%A Chuang, Yung-Sung
%A Glass, James
%Y Graham, Yvette
%Y Purver, Matthew
%S Findings of the Association for Computational Linguistics: EACL 2024
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F fang-etal-2024-joint
%X Passage retrieval is a crucial component of modern open-domain question answering (QA) systems, providing information for downstream QA components to generate accurate and transparent answers. In this study we focus on passage re-ranking, proposing a simple yet effective method, Joint Passage Re-ranking (JPR), that optimizes the mutual information between query and passage distributions, integrating both cross-encoders and generative models in the re-ranking process. Experimental results demonstrate that JPR outperforms conventional re-rankers and language model scorers in both open-domain QA retrieval settings and diverse retrieval benchmarks under zero-shot settings.
%U https://aclanthology.org/2024.findings-eacl.151
%P 2289-2298

Markdown (Informal)

[Joint Inference of Retrieval and Generation for Passage Re-ranking](https://aclanthology.org/2024.findings-eacl.151) (Fang et al., Findings 2024)

• Joint Inference of Retrieval and Generation for Passage Re-ranking (Fang et al., Findings 2024)

ACL

• Wei Fang, Yung-Sung Chuang, and James Glass. 2024. Joint Inference of Retrieval and Generation for Passage Re-ranking. In Findings of the Association for Computational Linguistics: EACL 2024, pages 2289–2298, St. Julian’s, Malta. Association for Computational Linguistics.