标题： VeriCoT：通过逻辑一致性检查的神经符号思维链验证 显示英文标题

标题： VeriCoT: Neuro-symbolic Chain-of-Thought Validation via Logical Consistency Checks

摘要： 大型语言模型可以通过思维链（CoT）执行多步骤推理，但它们无法可靠地验证自己的逻辑。 即使它们得出正确的答案，其背后的推理可能有误，这在高风险场景中会削弱信任。 为缓解这一问题，我们引入了VeriCoT，这是一种神经符号方法，能够从CoT推理中提取并验证形式化的逻辑论证。 VeriCoT将每个CoT推理步骤形式化为一阶逻辑，并识别出将论证扎根于源上下文、常识知识或先前推理步骤的前提。 符号表示使得自动化求解器可以验证逻辑有效性，而自然语言前提则使人类和系统能够识别缺乏依据或存在谬误的推理步骤。 在ProofWriter、LegalBench和BioASQ数据集上的实验表明，VeriCoT能有效识别有缺陷的推理，并作为最终答案正确性的强大预测器。 我们还利用VeriCoT的验证信号进行： (1) 推理时的自我反思，(2) 在VeriCoT提炼的数据集上进行监督微调（SFT），以及 (3) 使用基于验证的成对奖励进行直接偏好优化（DPO）的偏好微调（PFT），从而进一步提高推理的有效性和准确性。 显示英文摘要

摘要： LLMs can perform multi-step reasoning through Chain-of-Thought (CoT), but they cannot reliably verify their own logic. Even when they reach correct answers, the underlying reasoning may be flawed, undermining trust in high-stakes scenarios. To mitigate this issue, we introduce VeriCoT, a neuro-symbolic method that extracts and verifies formal logical arguments from CoT reasoning. VeriCoT formalizes each CoT reasoning step into first-order logic and identifies premises that ground the argument in source context, commonsense knowledge, or prior reasoning steps. The symbolic representation enables automated solvers to verify logical validity while the NL premises allow humans and systems to identify ungrounded or fallacious reasoning steps. Experiments on the ProofWriter, LegalBench, and BioASQ datasets show VeriCoT effectively identifies flawed reasoning, and serves as a strong predictor of final answer correctness. We also leverage VeriCoT's verification signal for (1) inference-time self-reflection, (2) supervised fine-tuning (SFT) on VeriCoT-distilled datasets and (3) preference fine-tuning (PFT) with direct preference optimization (DPO) using verification-based pairwise rewards, further improving reasoning validity and accuracy.