aiqbench analysis
This research addresses the critical issue of context-sensitive factual hallucinations in LLM reasoning, proposing SSC-GRPO to enforce step-level self-consistency during multi-step problem solving. By optimizing rewards based on internal consistency across rollouts, the method significantly reduces errors where models fail despite possessing relevant knowledge. This advancement offers a concrete pathway for improving reliability and trustworthiness in complex AI inference tasks without relying solely on external verification tools.
Reasoning Error from Known Fact: Step-Level Self-Consistency Group Relative Policy Optimization for LLM
arXiv:2607.18915v1 Announce Type: new Abstract: With the rapid advancement of large language models (LLMs), modern systems not only possess strong foundational capabilities and extensive knowledge, but can also solve complex problems via long, multi-step reasoning. However, as reasoning traces become longer, LLMs may produce a substantial amount of hallucinated content during the reasoning process, which is often difficult to detect. In this work, we conduct a fine-grained analysis of hallucinations arising in LLM reasoning and find that the reasoning traces are particularly prone to Context-Sensitive Factual Hallucinations: cases where the model actually has the relevant knowledge, yet makes factual errors due to contextual interference during reasoning. To address this issue, we propose Step-level Self-Consistency Group Relative Policy Optimization (SSC-GRPO), which assigns step-level rewards to reasoning traces by computing self-consistency scores of individual steps across multiple rollouts. Compared with prior methods, SSC-GRPO achieves state-of-the-art performance on both mathematical reasoning benchmarks and hallucination leaderboards. Our results offer a new perspective for detecting and mitigating hallucinations in the reasoning process of large language models.
Abstract:With the rapid advancement of large language models (LLMs), modern systems not only possess strong foundational capabilities and extensive knowledge, but can also solve complex problems via long, multi-step reasoning. However, as reasoning traces become longer, LLMs may produce a substantial amount of hallucinated content during the reasoning process, which is often difficult to detect. In this work, we conduct a fine-grained analysis of hallucinations arising in LLM reasoning and find that the reasoning traces are particularly prone to Context-Sensitive Factual Hallucinations: cases where the model actually has the relevant knowledge, yet makes factual errors due to contextual interference during reasoning. To address this issue, we propose Step-level Self-Consistency Group Relative Policy Optimization (SSC-GRPO), which assigns step-level rewards to reasoning traces by computing self-consistency scores of individual steps across multiple rollouts. Compared with prior methods, SSC-GRPO achieves state-of-the-art performance on both mathematical reasoning benchmarks and hallucination leaderboards. Our results offer a new perspective for detecting and mitigating hallucinations in the reasoning process of large language models.
Computation and Language (cs.CL)
arXiv:2607.18915 [cs.CL]
(or arXiv:2607.18915v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.18915 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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