H-Neurons: On the Existence, Impact, and Origin of Hallucination-Associated Neurons in LLMs

• URL: http://arxiv.org/abs/2512.01797v2
• Date: Tue, 02 Dec 2025 07:08:39 GMT
• Title: H-Neurons: On the Existence, Impact, and Origin of Hallucination-Associated Neurons in LLMs
• Authors: Cheng Gao, Huimin Chen, Chaojun Xiao, Zhiyi Chen, Zhiyuan Liu, Maosong Sun,
• Abstract summary: We identify hallucination-associated neurons (H-Neurons) in large language models (LLMs)<n>In terms of identification, we demonstrate that a remarkably sparse subset of neurons can reliably predict hallucination occurrences.<n>In terms of behavioral impact, controlled interventions reveal that these neurons are causally linked to over-compliance behaviors.
• Score: 56.31565301428888
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) frequently generate hallucinations -- plausible but factually incorrect outputs -- undermining their reliability. While prior work has examined hallucinations from macroscopic perspectives such as training data and objectives, the underlying neuron-level mechanisms remain largely unexplored. In this paper, we conduct a systematic investigation into hallucination-associated neurons (H-Neurons) in LLMs from three perspectives: identification, behavioral impact, and origins. Regarding their identification, we demonstrate that a remarkably sparse subset of neurons (less than $0.1\%$ of total neurons) can reliably predict hallucination occurrences, with strong generalization across diverse scenarios. In terms of behavioral impact, controlled interventions reveal that these neurons are causally linked to over-compliance behaviors. Concerning their origins, we trace these neurons back to the pre-trained base models and find that these neurons remain predictive for hallucination detection, indicating they emerge during pre-training. Our findings bridge macroscopic behavioral patterns with microscopic neural mechanisms, offering insights for developing more reliable LLMs.

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