HalluGuard: Demystifying Data-Driven and Reasoning-Driven Hallucinations in LLMs
The reliability of Large Language Models (LLMs) in high-stakes domains such as healthcare, law, and scientific discovery is often compromised by hallucinations. These failures typically stem from two sources: data-driven hallucinations and reasoning-driven hallucinations. However, existing detection methods usually address only one source and rely on task-specific heuristics, limiting their generalization to complex scenarios. To overcome these limitations, we introduce the Hallucination Risk Bound, a unified theoretical framework that formally decomposes hallucination risk into data-driven and reasoning-driven components, linked respectively to training-time mismatches and inference-time instabilities. This provides a principled foundation for analyzing how hallucinations emerge and evolve. Building on this foundation, we introduce HalluGuard, an NTK-based score that leverages the induced geometry and captured representations of the NTK to jointly identify data-driven and reasoning-driven hallucinations. We evaluate HalluGuard on 10 diverse benchmarks, 11 competitive baselines, and 9 popular LLM backbones, consistently achieving state-of-the-art performance in detecting diverse forms of LLM hallucinations.
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Xinyue Zeng
claimed_verified
2026-01-27T13:56:37.622Z
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/avatars/683bb03e904d85585cc5fe901b2f2d7c.svg
Xinyue Zeng
xyzeng2000
user
xyzeng2000
6978adf7026bdf04731170a5
Junhong Lin
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Yujun Yan
admin_assigned
2026-01-27T14:41:28.477Z
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yanyujun
yanyujun
user
yanyujun
6978adf7026bdf04731170a7
Feng Guo
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Liang Shi
admin_assigned
2026-01-27T14:41:18.235Z
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Liang Shi
liangshi
user
liangshi
6978adf7026bdf04731170a9
Jun Wu
6978adf7026bdf04731170aa
Dawei Zhou
HalluGuard: Demystifying Data-Driven and Reasoning-Driven Hallucinations in LLMs
The reliability of Large Language Models (LLMs) in high-stakes domains such as healthcare, law, and scientific discovery is often compromised by hallucinations. These failures typically stem from two sources: data-driven hallucinations and reasoning-driven hallucinations. However, existing detection methods usually address only one source and rely on task-specific heuristics, limiting their generalization to complex scenarios. To overcome these limitations, we introduce the Hallucination Risk Bound, a unified theoretical framework that formally decomposes hallucination risk into data-driven and reasoning-driven components, linked respectively to training-time mismatches and inference-time instabilities. This provides a principled foundation for analyzing how hallucinations emerge and evolve. Building on this foundation, we introduce HalluGuard, an NTK-based score that leverages the induced geometry and captured representations of the NTK to jointly identify data-driven and reasoning-driven hallucinations. We evaluate HalluGuard on 10 diverse benchmarks, 11 competitive baselines, and 9 popular LLM backbones, consistently achieving state-of-the-art performance in detecting diverse forms of LLM hallucinations.
A theoretical framework and detection method for identifying hallucinations in large language models by analyzing data-driven and reasoning-driven components through neural tangent kernel-based scoring.
Hallucination Risk Bound Neural Tangent Kernel NTK-based score data-driven hallucinations reasoning-driven hallucinations large language models hallucination detection