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.
In AI, a “hallucination” is just as much “there” as a non-“hallucination.” It’s a way for scientists to stomp their foot and say that the wrong output is the computer’s fault and not a natural consequence of how LLMs work.
A breeder reactor is creating something, which is like the outcome of breeding. That name fits.
a hallucination is seeing something that’s not there, which also fits.
In AI, a “hallucination” is just as much “there” as a non-“hallucination.” It’s a way for scientists to stomp their foot and say that the wrong output is the computer’s fault and not a natural consequence of how LLMs work.
Hallucinations requires perception. LLMs are just statistical models and do not have perceptions.
It was a cute name early on, now it is used to deflect when the output is just plain wrong.