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.- Anthology ID:
- 2024.findings-eacl.151
- Volume:
- Findings of the Association for Computational Linguistics: EACL 2024
- Month:
- March
- Year:
- 2024
- Address:
- St. Julian’s, Malta
- Editors:
- Yvette Graham, Matthew Purver
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2289–2298
- Language:
- URL:
- https://aclanthology.org/2024.findings-eacl.151
- DOI:
- Cite (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.
- Cite (Informal):
- Joint Inference of Retrieval and Generation for Passage Re-ranking (Fang et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.findings-eacl.151.pdf