Hybrid And Collaborative Passage Reranking

Abstract

In passage retrieval system, the initial passage retrieval results may be unsatisfactory, which can be refined by a reranking scheme. Existing solutions to passage reranking focus on enriching the interaction between query and each passage separately, neglecting the context among the top-ranked passages in the initial retrieval list. To tackle this problem, we propose a Hybrid and Collaborative Passage Reranking (HybRank) method, which leverages the substantial similarity measurements of upstream retrievers for passage collaboration and incorporates the lexical and semantic properties of sparse and dense retrievers for reranking. Besides, built on off-the-shelf retriever features, HybRank is a plug-in reranker capable of enhancing arbitrary passage lists including previously reranked ones. Extensive experiments demonstrate the stable improvements of performance over prevalent retrieval and reranking methods, and verify the effectiveness of the core components of HybRank.

Stats

• citations1
• S2 citations—
• github stars0
• HF likes0
• heat score2.26
• arxiv keyzhang2023hybrid

Related papers

• HYRR: Hybrid Infused Reranking For Passage Retrieval (2022)0.00
• Rank-k: Test-time Reasoning For Listwise Reranking (2025)0.00
• A Passage-based Approach To Learning To Rank Documents (2019)8.60
• Improving Passage Retrieval With Zero-shot Question Generation (2022)12.87
• Towards Robust Ranker For Text Retrieval (2022)5.84
• Refit: Relevance Feedback From A Reranker During Inference (2023)0.00
• A Study On Passage Re-ranking In Embedding Based Unsupervised Semantic Search (2018)0.00
• Drowning In Documents: Consequences Of Scaling Reranker Inference (2024)0.00