Evidence-Aligned Entity Verification for Hallucination Detection in Retrieval-Augmented Generation

2026-09-08

Annual Meeting of the Association for Computational Linguistics

AI Summary

本文针对基于检索增强生成的幻觉检测问题，提出了一种新的方法EAEV，通过将生成的实体与检索到的证据对齐来检测实体级别的幻觉。

Abstract

Hallucination detection is crucial for large language models (LLMs), as hallucinated content creates significant barriers in applications requiring factual accuracy. Current detection methods mainly depend on internal signals like uncertainty and self-consistency checks, using the model's pre-trained knowledge to identify unreliable outputs. However, pre-trained knowledge may become outdated and has coverage limitations, especially for specialized or recent information. To address these limitations, retrieval-augmented generation (RAG) has emerged as a promising solution by retrieving relevant evidence at inference time, grounding outputs beyond the model's parametric knowledge. In this paper, we target a critical and practical learning problem RAG-based hallucination detection (RHD), where RAG is employed to enhance hallucination detection by addressing information updating challenges. To address RHD, we propose a novel method Evidence-Aligned Entity Verification (EAEV), which detects entity-level hallucinations by leveraging RAG to align generated entities with retrieved evidence contexts. Specifically, EAEV evaluates entity-evidence alignment through three complementary dimensions and introduces counterfactual stability analysis to ensure robust alignments under evidence perturbations. Experiments across multiple RAG benchmarks demonstrate that EAEV achieves consistent improvements over existing methods with strong generalization capabilities.

Problem

• hallucination detection
• retrieval-augmented generation
• pre-trained knowledge
• factual accuracy
• evidence retrieval

Innovation

• Evidence-Aligned Entity Verification
• Retrieval-Augmented Generation
• Hallucination Detection
• Counterfactual Stability Analysis