Papers by Aoqi Zuo
FACT-E: Causality-Inspired Evaluation for Trustworthy Chain-of-Thought Reasoning (2026.findings-acl)
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| Challenge: | Existing models generate explanations that appear coherent while containing unfaithful intermediate steps. |
| Approach: | They propose a causality-inspired framework for evaluating CoT quality using controlled perturbations as an instrumental signal to separate genuine step-to-step dependence from bias-driven artifacts. |
| Outcome: | Experiments on GSM8K, MATH, and CommonsenseQA show that FACT-E improves reasoning-trajectory selection and yields stronger in-context learning exemplars. |
CausalAbstain: Enhancing Multilingual LLMs with Causal Reasoning for Trustworthy Abstention (2025.findings-acl)
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| Challenge: | Existing methods to reduce hallucinations in large language models are inaccurate and inaccuracies in the generated feedback. |
| Approach: | They propose a method that helps LLMs determine whether to utilize multiple generated feedback responses and how to identify the most useful ones. |
| Outcome: | Extensive experiments show that the proposed method outperforms baselines on encyclopedic and commonsense knowledge QA tasks. |