Hallucinations pose a significant obstacle to the reliability and widespread adoption of language models, yet their accurate measurement remains a persistent challenge. While many task- and domain-specific metrics have been proposed to assess faithfulness and factuality concerns, the robustness and generalization of these metrics are still untested. In this paper, we conduct a large-scale empirical evaluation of 6 diverse sets of hallucination detection metrics across 4 datasets, 37 language models from 5 families, and 5 decoding methods. Our extensive investigation reveals concerning gaps in current hallucination evaluation: metrics often fail to align with human judgments, take an overtly myopic view of the problem, and show inconsistent gains with parameter scaling. Encouragingly, LLM-based evaluation, particularly with GPT-4, yields the best overall results, and mode-seeking decoding methods seem to reduce hallucinations, especially in knowledge-grounded settings. These findings underscore the need for more robust metrics to understand and quantify hallucinations, and better strategies to mitigate them.
@article{arxiv.2504.18114,
title = {Evaluating Evaluation Metrics -- The Mirage of Hallucination Detection},
author = {Atharva Kulkarni and Yuan Zhang and Joel Ruben Antony Moniz and Xiou Ge and Bo-Hsiang Tseng and Dhivya Piraviperumal and Swabha Swayamdipta and Hong Yu},
journal= {arXiv preprint arXiv:2504.18114},
year = {2025}
}