Papers by Yuxi Sun
SHARP: Unlocking Interactive Hallucination via Stance Transfer in Role-Playing LLMs (2025.findings-acl)
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| Challenge: | Existing studies on social interactions neglect hallucination while struggling with poor generalizability and implicit character fidelity judgments. |
| Approach: | They propose a generalizable and explicit paradigm for uncovering interactive patterns of Large Language Models across diverse worldviews by defining interactive hallucination through stance transfer and SHARP, a benchmark built by extracting relations from commonsense knowledge graphs. |
| Outcome: | The proposed paradigm is generalizable and explicit and demonstrates its effectiveness and stability. |
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. |
REFLEX: Self-Refining Explainable Fact-Checking via Verdict-Anchored Style Control (2026.acl-long)
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| Challenge: | Existing methods for automated fact-checking often overlook deceptive misinformation styles in generated explanations. |
| Approach: | They propose a framework that explicitly controls reasoning style by anchoring explanations to the predicted verdict. |
| Outcome: | The proposed framework achieves state-of-the-art under LLaMA-series models with 465 samples. |
Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction (2024.emnlp-main)
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| Challenge: | Existing approaches to condensing textual information into concise and structured tables are limited in their applicability in broader contexts. |
| Approach: | They propose a benchmark dataset for generating summary tables of competitions based on real-time commentary texts that incorporates large-scale textual information into concise and structured tables. |
| Outcome: | The proposed method exhibits strong generalization abilities, surpassing previous approaches on several other text-to-table datasets. |
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. |