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

Maosong Sun and his team was fascinated by hallucinating neurons!

From the 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."

[2512.01797] H-Neurons: On the Existence, Impact, and Origin of Hallucination-Associated Neurons in LLMs