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

• URL: http://arxiv.org/abs/2510.19318v1
• Date: Wed, 22 Oct 2025 07:28:37 GMT
• Title: HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination Taxonomy
• Authors: Fan Xu, Xinyu Hu, Zhenghan Yu, Li Lin, Xu Zhang, Yang Zhang, Wei Zhou, Jinjie Gu, Xiaojun Wan,
• Abstract summary: We introduce a comprehensive hallucination taxonomy with 11 categories across various NLG tasks.<n>We propose the HAllucination Detection (HAD) models, which integrate hallucination detection, span-level identification, and correction into a single inference process.
• Score: 48.68088917291552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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