Joint Inference of Retrieval and Generation for Passage Re-ranking

Anonymous

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.

Paper Type: short

Research Area: Information Retrieval and Text Mining

Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models

Languages Studied: English