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

ACL ARR 2026 March Submission2157 Authors

Keywords: Authority Perception, LLM, RAG

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.

Paper Type: Long

Research Area: Language Modeling

Research Area Keywords: retrieval-augmented generation, benchmarking

Contribution Types: Model analysis & interpretability, Data resources

Languages Studied: English

Submission Number: 2157