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

arXiv: https://arxiv.org/abs/2603.25092 · HTML: https://mlsanity.com/paper/2603.25092/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation.
- Builds on 17 of its 32 references that are in the map.
- First posted on arXiv 26 Mar 2026.

## Placed in

- Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/)

## Builds on (most cited first, 17 of 17)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories](https://mlsanity.com/paper/2212.10511/) (2022)
- [From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge](https://mlsanity.com/paper/2411.16594/) (2024)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Bridging the Preference Gap between Retrievers and LLMs](https://mlsanity.com/paper/2401.06954/) (2024)
- [Are Large Language Models Good at Utility Judgments?](https://mlsanity.com/paper/2403.19216/) (2024)
- [CrAM: Credibility-Aware Attention Modification in LLMs for Combating Misinformation in RAG](https://mlsanity.com/paper/2406.11497/) (2024)
- [Not All Contexts Are Equal: Teaching LLMs Credibility-aware Generation](https://mlsanity.com/paper/2404.06809/) (2024)
- [A Survey on Predicting the Factuality and the Bias of News Media](https://mlsanity.com/paper/2103.12506/) (2021)
- [Retrieval-Augmented Generation with Estimation of Source Reliability](https://mlsanity.com/paper/2410.22954/) (2024)
- [Generating Media Background Checks for Automated Source Critical Reasoning](https://mlsanity.com/paper/2409.00781/) (2024)
- [Popular but Wrong: Understanding and Mitigating LLM Overconfidence through Knowledge Popularity](https://mlsanity.com/paper/2505.17537/) (2025)
- [Utility-Focused LLM Annotation for Retrieval and Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.05220/) (2025)
- [An Iterative Utility Judgment Framework Inspired by Philosophical Relevance via LLMs](https://mlsanity.com/paper/2406.11290/) (2024)
- [LLM-Driven Usefulness Judgment for Web Search Evaluation](https://mlsanity.com/paper/2504.14401/) (2025)
- [Modeling Contextual Passage Utility for Multihop Question Answering](https://mlsanity.com/paper/2512.06464/) (2025)

## Built on by (most cited first, 1 of 1)

- [Benchmarking and Training Reward Models for Cross-Source Evidence Grounding in Search Agents](https://mlsanity.com/paper/2610.17827/) (2026)

## Abstract (arXiv)

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.