Papers by Yongqiang Chen
CiPO: Counterfactual Unlearning for Large Reasoning Models through Iterative Preference Optimization (2026.acl-long)
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| Challenge: | Existing methods to unlearning large reasoning models do not remove unwanted knowledge from CoT traces or interfere with the reasoning process. |
| Approach: | They propose a framework that targets the CoT reasoning in Large Reasoning Models by generating a valid counterfactual reasoning trace for preference tuning. |
| Outcome: | Experiments on large LRMs show that CiPO completely removes knowledge from the intermediate CoT steps and the final answer while preserving the reasoning abilities of LRM. |
Paraphrase Makes Perfect: Leveraging Expression Paraphrase to Improve Implicit Sentiment Learning (2025.coling-main)
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| Challenge: | Existing implicit sentiment learning methods focus on capturing implicit sentiment knowledge individually, without considering the potential connection between implicit and explicit sentiment. |
| Approach: | They propose an expression paraphrase strategy and a sentiment-consistent contrastive learning mechanism to learn the connections between implicit and explicit sentiment expressions and integrate them into the model. |
| Outcome: | The proposed method is effective on implicit sentiment analysis on public datasets. |
Retrieval-Augmented Generation with Hierarchical Knowledge (2025.findings-emnlp)
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Haoyu Huang, Yongfeng Huang, Yang Junjie, Zhenyu Pan, Yongqiang Chen, Kaili Ma, Hongzhi Chen, James Cheng
| Challenge: | Existing RAG methods do not utilize hierarchical knowledge in human cognition, which limits the capabilities of RAG systems. |
| Approach: | They propose a graph-based approach that utilizes hierarchical knowledge to enhance the semantic understanding and structure capturing capabilities of RAG systems. |
| Outcome: | The proposed approach achieves significant performance improvements over the state-of-the-art methods. |