HalluGuard: Demystifying Data-Driven and Reasoning-Driven Hallucinations in LLMs

Xinyue Zeng, Junhong Lin, yanyujun, Feng Guo, Liang Shi, Jun Wu, Dawei Zhou

HalluGuard: Demystifying Data-Driven and Reasoning-Driven Hallucinations in LLMs: 1 upvotes on Hugging Face Daily Papers, #38 of 40 papers on 2026-01-27. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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