VeriCoT: Neuro-symbolic Chain-of-Thought Validation via Logical
  Consistency Checks
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.
690e232f41eeb14959b21d01
Yu Feng
690e232f41eeb14959b21d02
Nathaniel Weir
690e232f41eeb14959b21d03
Kaj Bostrom
690e232f41eeb14959b21d04
Sam Bayless
690e232f41eeb14959b21d05
Darion Cassel
690e232f41eeb14959b21d06
Sapana Chaudhary
690e232f41eeb14959b21d07
Benjamin Kiesl-Reiter
690e232f41eeb14959b21d08
Huzefa Rangwala
VeriCoT: Neuro-symbolic Chain-of-Thought Validation via Logical
  Consistency Checks
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.
VeriCoT, a neuro-symbolic method, formalizes and verifies logical arguments in Chain-of-Thought reasoning to improve the reliability and accuracy of LLMs.
Chain-of-Thought VeriCoT neuro-symbolic method first-order logic formal logical arguments source context commonsense knowledge automated solvers inference-time self-reflection supervised fine-tuning preference fine-tuning direct preference optimization verification-based pairwise rewards