AuthorityBench: Benchmarking LLM Authority Perception for Reliable Retrieval-Augmented Generation

TL;DR

This paper introduces AuthorityBench, a benchmark to evaluate LLMs' ability to perceive information authority, demonstrating its importance for improving retrieval accuracy and reducing misinformation in knowledge-based tasks.

Contribution

We present AuthorityBench, a comprehensive benchmark with datasets and evaluation methods to assess LLM authority perception, highlighting its significance for reliable retrieval-augmented generation.

Findings

ListJudge and PairJudge with PointScore are most correlated with ground-truth authority.

Incorporating webpage text degrades judgment performance, indicating authority is distinct from textual style.

Authority-guided filtering improves answer accuracy in downstream RAG tasks.

Abstract

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) with external knowledge but remains vulnerable to low-authority sources that can propagate misinformation. We investigate whether LLMs can perceive information authority - a capability extending beyond semantic understanding. To address this, we introduce AuthorityBench, a comprehensive benchmark for evaluating LLM authority perception comprising three datasets: DomainAuth (10K web domains with PageRank-based authority), EntityAuth (22K entities with popularity-based authority), and RAGAuth (120 queries with documents of varying authority for downstream evaluation). We evaluate five LLMs using three judging methods (PointJudge, PairJudge, ListJudge) across multiple output formats. Results show that ListJudge and PairJudge with PointScore output achieve the strongest correlation with ground-truth authority, while ListJudge offers optimal cost-effectiveness. Notably, incorporating webpage text consistently degrades judgment performance, suggesting authority is distinct from textual style. Downstream experiments on RAG demonstrate that authority-guided filtering largely improves answer accuracy, validating the practical importance of authority perception for reliable knowledge retrieval. Code and benchmark are available at: https://github.com/Trustworthy-Information-Access/AuthorityBench.