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

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.

@article{guo2025_2404.11960,
  title={ MCRanker: Generating Diverse Criteria On-the-Fly to Improve Point-wise LLM Rankers },
  author={ Fang Guo and Wenyu Li and Honglei Zhuang and Yun Luo and Yafu Li and Le Yan and Qi Zhu and Yue Zhang },
  journal={arXiv preprint arXiv:2404.11960},
  year={ 2025 }
}