Year: 2026
Type: conference-paper
Abstract: Chain-of-Thought (CoT) prompting has improved LLM reasoning, but models often generate explanations that appear coherent while containing unfaithful intermediate steps.Existing self-evaluation approaches are prone to inherent biases: the model may confidently endorse coherence even when the step-to-step implication is not valid, leading to unreliable faithfulness evaluation.We propose FACT-E, a causality-inspired framework for evaluating CoT quality.FACT-E uses controlled perturbations as an instrumental signal to separate genuine step-to-step dependence from biasdriven artifacts, producing more reliable faithfulness estimates (intra-chain faithfulness).To select trustworthy trajectories, FACT-E jointly considers intra-chain faithfulness and CoTto-answer consistency, ensuring that selected chains are both faithful internally and supportive of the correct final answer.Experiments on GSM8K, MATH, and CommonsenseQA show that FACT-E improves reasoning-trajectory selection and yields stronger in-context learning exemplars.FACT-E also reliably detects flawed reasoning under noisy conditions, providing a robust metric for trustworthy LLM reasoning 1 .
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166878707