Precise Zero-Shot Pointwise Ranking with LLMs through Post-Aggregated Global Context Information
Kehan Long, Shasha Li, Chen Xu, Jintao Tang, Ting Wang

TL;DR
This paper introduces a novel zero-shot pointwise ranking method for large language models that incorporates global context through anchor-based contrastive scoring, significantly improving effectiveness while maintaining efficiency.
Contribution
The paper proposes GCCP and PAGC strategies that integrate global reference comparisons into pointwise ranking, enhancing performance without increasing computational costs.
Findings
Outperforms previous pointwise methods on TREC DL and BEIR benchmarks.
Achieves competitive results with more resource-intensive comparative methods.
Maintains efficiency comparable to traditional pointwise approaches.
Abstract
Recent advancements have successfully harnessed the power of Large Language Models (LLMs) for zero-shot document ranking, exploring a variety of prompting strategies. Comparative approaches like pairwise and listwise achieve high effectiveness but are computationally intensive and thus less practical for larger-scale applications. Scoring-based pointwise approaches exhibit superior efficiency by independently and simultaneously generating the relevance scores for each candidate document. However, this independence ignores critical comparative insights between documents, resulting in inconsistent scoring and suboptimal performance. In this paper, we aim to improve the effectiveness of pointwise methods while preserving their efficiency through two key innovations: (1) We propose a novel Global-Consistent Comparative Pointwise Ranking (GCCP) strategy that incorporates global reference…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsText and Document Classification Technologies · Information Retrieval and Search Behavior · Sentiment Analysis and Opinion Mining
