Papers by Yuxing Tian
ReAttn: Improving Attention-based Re-ranking via Attention Re-weighting (2026.findings-eacl)
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| Challenge: | Attention-based re-ranking methods are highly concentrated a small subset of tokens within a few documents, making others indistinguishable. |
| Approach: | They propose a post-hoc re-weighting strategy that uses attention weights to reduce lexical bias and emphasize distinctive terms. |
| Outcome: | The proposed method reduces lexical bias and emphasizes distinctive terms across documents, while maintaining a balanced distribution across informative tokens. |
Context-Fidelity Boosting: Enhancing Faithful Generation through Watermark-Inspired Decoding (2026.findings-acl)
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Weixu Zhang, Fanghua Ye, Qiang Gao, Jian Li, Haolun Wu, Yuxing Tian, Sijing Duan, Nan Du, Xiaolong Li
| Challenge: | Large language models produce content that contradicts or overlooks information provided in the input context, a phenomenon known as faithfulness hallucination. |
| Approach: | They propose a lightweight framework that boosts the generation probability of context-relevant tokens by boosting the generation of tokens. |
| Outcome: | The proposed framework improves faithfulness metrics with minimal generation overhead. |
Preference Heads in Large Language Models: A Mechanistic Framework for Interpretable Personalization (2026.acl-long)
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Weixu Zhang, Ye Yuan, Changjiang Han, Yuxing Tian, Zipeng Sun, Linfeng Du, Jikun Kang, Hong Kang, Xue Liu, Haolun Wu
| Challenge: | Large Language Models exhibit strong implicit personalization ability, but most approaches treat this behavior as a black box. |
| Approach: | They propose a mechanistic interpretation perspective and propose 'sparse' set of Preference Heads . they compute a Preference Contribution Score for each attention head and compare their predictions . |
| Outcome: | The proposed framework computes a Preference Contribution Score (PCS) for each attention head and measures its causal impact on user aligned outputs. |