Papers by Shiyu Liang
From Domains to Instances: Dual-Granularity Data Synthesis for LLM Unlearning (2026.findings-acl)
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Xiaoyu Xu, Minxin Du, Zitong LI, Zi Liang, Zhibiao Guo, Zhang Shiyu, Peizhao Hu, Qingqing Ye, Haibo Hu
| Challenge: | Currently, the evaluation of unlearning is limited due to the lack of granularity in the model. |
| Approach: | They propose a framework for synthesizing high-quality forget sets that exploits the target model per se to elicit data that matches its internal knowledge distribution through seed-guided and adversarial prompting. |
| Outcome: | The proposed framework achieves a superior balance of relevance, diversity, and efficiency across benchmarks. |
A Middle Path for On-Premises LLM Deployment: Preserving Privacy Without Sacrificing Model Confidentiality (2025.emnlp-main)
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| Challenge: | Privacy-sensitive users require deploying large language models within their own infrastructure (on-premises) vulnerabilities in local environments can lead to unauthorized access and potential model theft. |
| Approach: | They propose a framework that secures a few bottom layers in a secure environment . they propose metric to optimize trade-off between protection and customization flexibility . |
| Outcome: | The proposed framework outperforms baselines on five models with 1.3B to 70B parameters. |
Multi-Level Cross-Modal Alignment for Speech Relation Extraction (2024.emnlp-main)
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Liang Zhang, Zhen Yang, Biao Fu, Ziyao Lu, Liangying Shao, Shiyu Liu, Fandong Meng, Jie Zhou, Xiaoli Wang, Jinsong Su
| Challenge: | Existing studies use synthetic speech to train and evaluate SpeechRE models, hindering their development . modality gap issue limits performance of existing models, limiting future researches . |
| Approach: | They propose to use speech data to train and evaluate SpeechRE models by using real speech . they propose to train a cross-modal alignment model to bridge the modality gap . |
| Outcome: | The proposed model can train to bridge the modality gap between speech encoder and text decoder . the proposed model is based on two real SpeechRE datasets . |
Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets (P19-1)
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| Challenge: | Natural Language Sentence Matching (NLSM) is a popular NLP task. |
| Approach: | They propose to use QuoraQP to train and evaluate NLSM models using a selection bias framework. |
| Outcome: | The proposed framework can improve generalization ability of trained models and give more trustworthy evaluation results for real-world adoptions. |
VCORE: Variance-Controlled Optimization-based Reweighting for Chain-of-Thought Supervision (2026.acl-long)
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| Challenge: | Empirical evaluations demonstrate that VCORE achieves the strongest overall average performance, with especially clear gains on lower-capacity models. |
| Approach: | They propose a framework that reformulates supervision as a constrained optimization problem. |
| Outcome: | Empirical evaluations show that VCORE achieves the strongest overall average performance, with especially clear gains on lower-capacity models. |