Papers by Ziqian Liu
Gradient-Guided Multi-Judge Prompt Optimization (2026.acl-long)
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ChenZhuo Zhao, Xinda Wang, Pu Zhao, Yue Huang, Junting Lu, Ziqian Liu, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing approaches to prompt optimization trade off signal quality against computational cost. |
| Approach: | They propose a framework that uses a first-order gradient approximation to score segment importance in a continuous masking direction. |
| Outcome: | The proposed framework improves efficiency and robustness by using a first-order gradient approximation to score segment importance in a continuous masking direction. |
LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement (2025.acl-long)
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Boyi Kang, Xinfa Zhu, Zihan Zhang, Zhen Ye, Mingshuai Liu, Ziqian Wang, Yike Zhu, Guobin Ma, Jun Chen, Longshuai Xiao, Chao Weng, Wei Xue, Lei Xie
| Challenge: | Recent advances in language models have demonstrated strong capabilities in semantic understanding and contextual modeling. |
| Approach: | They propose a LLaMA-based language model that incentivizes generalization capabilities for speech enhancement. |
| Outcome: | The proposed language model outperforms prior task-specific discriminative and generative models in acoustic enhancement tasks. |
PMPO: Probabilistic Metric Prompt Optimization for Small and Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing methods evaluate candidate prompts by sampling full outputs, often coupled with self critique or human annotated preferences, which limits scalability, especially for smaller models or models that are not instruction tuned. |
| Approach: | They propose a framework that uses token level cross entropy as a direct, lightweight evaluation signal to evaluate candidate prompts. |
| Outcome: | The proposed framework outperforms prior prompt optimizers across model sizes and datasets. |
A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification (N19-1)
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| Challenge: | Existing weakly supervised methods for document-level multi-aspect sentiment classification are not easy to obtain. |
| Approach: | They propose a variational approach to weakly supervised document-level multi-aspect sentiment classification using target-opinion word pairs as "supervision" they aim to learn a sentiment polarity classifier by optimizing the lower bound . |
| Outcome: | The proposed method outperforms weakly supervised baselines on TripAdvisor and BeerAdvocate datasets and can be comparable to state-of-the-art supervised methods with hundreds of labels per aspect. |