Think Thrice Before Reranking: Multi-perspective Evidence and Reasoning Integration for Text Reranking
MERIT-Rank integrates multiple LLM reasoning trajectories for text reranking; its 4B model outperforms most 7B and 32B rerankers on BRIGHT.
MERIT-Rank formulates a Multi-Trajectory Reasoning Space that evaluates query-document relevance from multiple perspectives and consolidates reasoning paths with a joint reranker. A progressive training framework, Progressive Rank Policy Optimization (PRPO), stabilizes reasoning trajectories while improving ranking quality through staged objectives. Experiments on reasoning-intensive and traditional retrieval benchmarks show consistent gains over competitive baselines, with the 4B model outperforming most 7B and even 32B rerankers on BRIGHT.
- Multi-trajectory reasoning space evaluates relevance from multiple perspectives
- PRPO progressive training stabilizes reasoning with staged objectives
- 4B model outperforms most 7B and 32B rerankers on BRIGHT
- Gains consistent on reasoning-intensive and traditional retrieval benchmarks
From the original article
1 min read at arxiv.orgReasoning-based reranking with Large Language Models (LLMs) has shown promising improvements in text ranking. However, current methods predominantly rely on a single reasoning trajectory, resulting in rankings that are susceptible to reasoning errors and inherently constrained in modeling the multifaceted signals underlying document relevance.
By Lijun Liu · arxiv.org
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.