# From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification

arXiv: https://arxiv.org/abs/2310.11675 · HTML: https://mlsanity.com/paper/2310.11675/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2023.

## Built on by (most cited first, 15 of 15)

- [Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models](https://mlsanity.com/paper/2403.06448/) (2024)
- [Claim Verification in the Age of Large Language Models: A Survey](https://mlsanity.com/paper/2408.14317/) (2024)
- [Multi-granular Adversarial Attacks against Black-box Neural Ranking Models](https://mlsanity.com/paper/2404.01574/) (2024)
- [Are Large Language Models Good Fact Checkers: A Preliminary Study](https://mlsanity.com/paper/2311.17355/) (2023)
- [The Next Phase of Scientific Fact-Checking: Advanced Evidence Retrieval from Complex Structured Academic Papers](https://mlsanity.com/paper/2506.20844/) (2025)
- [Veri-R1: Toward Precise and Faithful Claim Verification via Online Reinforcement Learning](https://mlsanity.com/paper/2510.01932/) (2025)
- [LLM-Specific Utility for Retrieval-Augmented Generation](https://mlsanity.com/paper/2510.11358/) (2025)
- [An Iterative Utility Judgment Framework Inspired by Philosophical Relevance via LLMs](https://mlsanity.com/paper/2406.11290/) (2024)
- [A Benchmark for Open-Domain Numerical Fact-Checking Enhanced by Claim Decomposition](https://mlsanity.com/paper/2510.22055/) (2025)
- [RIGHT: Retrieval-augmented Generation for Mainstream Hashtag Recommendation](https://mlsanity.com/paper/2312.10466/) (2023)
- [+VeriRel: Verification Feedback to Enhance Document Retrieval for Scientific Fact Checking](https://mlsanity.com/paper/2508.11122/) (2025)
- [Improving Generated and Retrieved Knowledge Combination Through Zero-shot Generation](https://mlsanity.com/paper/2412.18800/) (2024)
- [Beyond Relevance: Utility-Centric Retrieval in the LLM Era](https://mlsanity.com/paper/2604.08920/) (2026)
- [Sparse Evidence Can Suffice: Agentic Evidence Seeking for Multimodal Video Misinformation Detection](https://mlsanity.com/paper/2607.18080/) (2026)
- [Validating FKG.in: Soundness Assessment in LLM-Augmented Indian Food Knowledge](https://mlsanity.com/paper/2608.29249/) (2026)

## Abstract (arXiv)

Retrieval-enhanced methods have become a primary approach in fact verification (FV); it requires reasoning over multiple retrieved pieces of evidence to verify the integrity of a claim. To retrieve evidence, existing work often employs off-the-shelf retrieval models whose design is based on the probability ranking principle. We argue that, rather than relevance, for FV we need to focus on the utility that a claim verifier derives from the retrieved evidence. We introduce the feedback-based evidence retriever(FER) that optimizes the evidence retrieval process by incorporating feedback from the claim verifier. As a feedback signal we use the divergence in utility between how effectively the verifier utilizes the retrieved evidence and the ground-truth evidence to produce the final claim label. Empirical studies demonstrate the superiority of FER over prevailing baselines.