The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs

ACL ARR 2025 May Submission6952 Authors

Abstract: Large language models (LLMs) have revolutionized natural language processing, yet their tendency to hallucinate poses serious challenges for reliable deployment. Despite numerous hallucination detection methods, their evaluations often rely on ROUGE, a metric based on lexical overlap that misaligns with human judgments. Through comprehensive human studies, we demonstrate that while ROUGE exhibits high recall, its extremely low precision leads to misleading performance estimates. In fact, several established detection methods show performance drops of up to 45.9\% when assessed using human-aligned metrics like LLM-as-Judge. Moreover, our analysis reveals that simple heuristics based on response length can rival complex detection techniques, exposing a fundamental flaw in current evaluation practices. We argue that adopting semantically aware and robust evaluation frameworks is essential to accurately gauge the true performance of hallucination detection methods, ultimately ensuring the trustworthiness of LLM outputs.

Paper Type: Long

Research Area: Interpretability and Analysis of Models for NLP

Research Area Keywords: misinformation detection, evaluation methodologies

Contribution Types: Model analysis & interpretability, Reproduction study, Data analysis

Languages Studied: english

Submission Number: 6952