H Neurons: On the Existence, Impact, and Origin of Hallucination Associated Neurons in LLMs
H-Neurons: On the Existence, Impact, and Origin of Hallucination-Associated Neurons in LLMs Cheng Gao, Huimin Chen, Chaojun Xiao, Zhiyi Chen, Zhiyuan Liu, Maosong Sun Tsinghua University {gaoc24}@mails.tsinghua.edu.cn , {huimchen,xcj,liuzy}@tsinghua.edu.cn 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.
Voice: ElevenLabs
Transcript
Izzo So here’s one that’s been making the rounds — H-Neurons: On the Existence, Impact, and Origin of Hallucination-Associated Neurons in LLMs.
Izzo You’re listening to Exploring Next. I’m Izzo, and Boone’s here. Let’s get into it.
Boone Yeah, this caught my attention because While prior work has examined hallucinations from macroscopic perspectives such as training data and objectives, the underlying neuron-level mechanisms remain largely unexplored.
Izzo From a product standpoint, the interesting question is who actually ships with this. Specifically, drawing from setups in previous work ( Finding_Safety_Neurons ; Finding_Skill_Neurons ; Detecting_hallu ) , we focus on neurons in the feedforward networks and examine hallucinations in knowledge-based question answering and make the following observations.
Boone Right, and technically We hypothesize that among the millions of neurons in modern LLMs, a sparse subset exhibits activation patterns that systematically distinguish between hallucinatory and faithful outputs.
Izzo Okay so what should people actually go try? The original source is a good starting point: https://arxiv.org/html/2512.01797v2
Boone Definitely read that first. And if you want to go deeper, look into related tools in the same space — build something small and see where it breaks.
Izzo Good call. That’s the episode — we’ll catch you on the next one.