arXiv cs.CL· Linghao Meng, Feng He, Xuan Yang, Junyuan Mao, Pinze Ren, Deqing Mu, Hesen Yang, Qiankun Li·· 10 天前AI 评分27
PHRBench：LLM 幻觉后推理的行为评估基准
PHRBench: A Behavioral Evaluation of Post-Hallucination Reasoning in LLMs
AI 导读
研究者提出 PHRBench，一个覆盖四个领域、18 个 LLM 的受控基准，用幻觉遵从、幻觉规避和启发式纠正三类行为刻画模型在幻觉前提下的推理轨迹。在 4820 个受控实例中，成功纠正并得出正确答案的情况相对罕见，且与推理过程中更频繁的信念更新相关。幻觉提示词本身的属性对成功恢复有显著预测力，轻量预测器 AUROC 达 0.847。
正文 · 原文
Abstract:Hallucinated information can propagate through multi-stage LLM systems and become part of the context for subsequent reasoning. Existing studies of post-hallucination reasoning (PHR) mainly characterize changes in final outcomes and aggregate reasoning dynamics, leaving how models resolve hallucinated premises at the response level insufficiently understood. In this work, we introduce PHRBench, a controlled benchmark for behaviorally structured PHR across four domains and 18 large language models. PHRBench characterizes each reasoning trajectory independently of final-answer correctness through Hallucination Compliance, Hallucination Avoidance, and Heuristic Correction, and defines an insightful trajectory as successful correction that ultimately reaches the correct answer. Across 4820 controlled instances, we find that successful recovery remains relatively rare and is associated with more frequent belief updates along the reasoning trajectory. We further find that properties of the hallucinated prompt contain substantial predictive signal for successful recovery, with a lightweight predictor achieving an AUROC of 0.847. These findings provide a behavioral view of post-hallucination reasoning, characterizing how LLMs resolve erroneous context and when successful recovery is likely to occur.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.10455 [cs.CL] |
| (or arXiv:2610.10455v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10455 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Linghao Meng [view email]
[v1]
Wed, 7 Oct 2026 17:25:23 UTC (7,818 KB)
来源：arXiv cs.CL · arxiv.org