Papers by Zipeng Wang
Warmup Generations: A Task-Agnostic Approach for Guiding Sequence-to-Sequence Learning with Unsupervised Initial State Generation (2025.acl-long)
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| Challenge: | Existing supervised fine-tuning (SFT) methods focus on directly generating the target output without leveraging the benefits of intermediate steps or initial guidance. |
| Approach: | They propose a task-agnostic framework that enables models to generate intermediate "warmup" sequences that are iteratively refined to maximize their contribution to the final output. |
| Outcome: | The proposed framework outperforms traditional supervised fine-tuning methods on translation, summarization, and multi-choice question answering tasks. |
Enhancing Visual Dialog Questioner with Entity-based Strategy Learning and Augmented Guesser (2021.findings-emnlp)
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| Challenge: | Existing methods to build a visual dialog (VD) Questioner do not provide explicit guidance for questioner to generate visually related and informative questions. |
| Approach: | They propose a Related entity enhanced Questioner that learns entity-based questioning strategy from human dialogs. |
| Outcome: | The proposed approach achieves state-of-the-art performance on image-guessing task and question diversity. |
Rapid Diffusion: Building Domain-Specific Text-to-Image Synthesizers with Fast Inference Speed (2023.acl-industry)
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Bingyan Liu, Weifeng Lin, Zhongjie Duan, Chengyu Wang, Wu Ziheng, Zhang Zipeng, Kui Jia, Lianwen Jin, Cen Chen, Jun Huang
| Challenge: | Text-to-Image Synthesis (TIS) aims to generate images based on textual inputs . but, current diffusion-based models lack entity knowledge and low inference speed . |
| Approach: | They propose a framework for training and deploying latent diffusion models with rich entity knowledge injected and optimized networks. |
| Outcome: | The proposed framework improves image quality and inference speed and can be used in industrial applications. |
Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study (2025.findings-emnlp)
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Yujun Zhou, Jiayi Ye, Zipeng Ling, Yufei Han, Yue Huang, Haomin Zhuang, Zhenwen Liang, Kehan Guo, Taicheng Guo, Xiangqi Wang, Xiangliang Zhang
| Challenge: | Existing benchmarks that rely on final-answer accuracy fail to capture the quality of the reasoning process. |
| Approach: | They propose a fine-grained evaluation framework that assesses logical reasoning across three dimensions: overall accuracy, stepwise soundness, and representation-level probing. |
| Outcome: | The proposed framework assesses logical reasoning across three dimensions: overall accuracy, stepwise soundness, and representation-level probing. |
Fuzzy Reasoning Chain (FRC): An Innovative Reasoning Framework from Fuzziness to Clarity (2025.findings-emnlp)
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Ping Chen, Xiang Liu, Zhaoxiang Liu, Zezhou Chen, Xingpeng Zhang, Huan Hu, Zipeng Wang, Kai Wang, Shuming Shi, Shiguo Lian
| Challenge: | ambiguity, polysemy, or uncertainty remain significant challenges in natural language processing. |
| Approach: | They introduce a framework that integrates LLM semantic priors with continuous fuzzy membership degrees to create an explicit interaction between probability-based reasoning and fuzzy membership reasoning. |
| Outcome: | The proposed framework integrates semantic priors with continuous fuzzy membership degrees . it allows ambiguous inputs to be gradually transformed into clear and interpretable decisions . |