MCRanker: Generating Diverse Criteria On-the-Fly to Improve Point-wise LLM Rankers

TL;DR

This paper introduces MCRanker, a multi-criteria approach that generates diverse, perspective-based scoring criteria to improve the accuracy and comprehensiveness of pointwise LLM rankers across multiple datasets.

Contribution

The paper proposes a novel multi-criteria ensemble method that dynamically generates diverse evaluation perspectives to enhance LLM ranking performance.

Findings

Significant performance improvements on BEIR datasets

Effective generation of diverse evaluation criteria

Addresses limitations of standard pointwise rankers

Abstract

The most recent pointwise Large Language Model (LLM) rankers have achieved remarkable ranking results. However, these rankers are hindered by two major drawbacks: (1) they fail to follow a standardized comparison guidance during the ranking process, and (2) they struggle with comprehensive considerations when dealing with complicated passages. To address these shortcomings, we propose to build a ranker that generates ranking scores based on a set of criteria from various perspectives. These criteria are intended to direct each perspective in providing a distinct yet synergistic evaluation. Our research, which examines eight datasets from the BEIR benchmark demonstrates that incorporating this multi-perspective criteria ensemble approach markedly enhanced the performance of pointwise LLM rankers.