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

Summary

[Submitted on 1 Dec 2025 (v1), last revised 2 Dec 2025 (this version, v2)] Title:H-Neurons: On the Existence, Impact, and Origin of Hallucination-Associated Neurons in LLMs View a PDF of the paper titled H-Neurons: On the Existence, Impact, and Origin of Hallucination-Associated Neurons in LLMs, by Cheng Gao and 5 other authors View PDF HTML (experimental) 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. Submission history From: Cheng Gao [view email] [v1] Mon, 1 Dec 2025 15:32:14 UTC (1,559 KB) [v2] Tue, 2 Dec 2025 07:08:39 UTC (1,559 KB)