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arXiv · 2510.19318

HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination Taxonomy

Fan Xu·Xinyu Hu·Zhenghan Yu·Li Lin·Xu Zhang·Yang Zhang·Wei Zhou·Jinjie Gu·Xiaojun Wan
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Abstract

The increasing reliance on natural language generation (NLG) models, particularly large language models, has raised concerns about the reliability and accuracy of their outputs. A key challenge is hallucination, where models produce plausible but incorrect information. As a result, hallucination detection has become a critical task. In this work, we introduce a comprehensive hallucination taxonomy with 11 categories across various NLG tasks and propose the HAllucination Detection (HAD) models https://github.com/pku0xff/HAD, which integrate hallucination detection, span-level identification, and correction into a single inference process. Trained on an elaborate synthetic dataset of about 90K samples, our HAD models are versatile and can be applied to various NLG tasks. We also carefully annotate a test set for hallucination detection, called HADTest, which contains 2,248 samples. Evaluations on in-domain and out-of-domain test sets show that our HAD models generally outperform the existing baselines, achieving state-of-the-art results on HaluEval, FactCHD, and FaithBench, confirming their robustness and versatility.

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Fan Xu, Xinyu Hu, Zhenghan Yu, Li Lin, Xu Zhang, Yang Zhang, Wei Zhou, Jinjie Gu, Xiaojun Wan. 2025-10-22. HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination Taxonomy. https://arxiv.org/abs/2510.19318

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