Papers by Zongyu Wu
VDebugger: Harnessing Execution Feedback for Debugging Visual Programs (2024.findings-emnlp)
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| Challenge: | Visual programs are executable code generated by large language models to address visual reasoning problems. |
| Approach: | They propose a critic-refiner framework that localizes and debugs visual programs by tracking execution step by step. |
| Outcome: | The proposed framework detects and corrects program errors leveraging detailed execution feedback, improving interpretability and accuracy. |
Divide-Verify-Refine: Can LLMs Self-align with Complex Instructions? (2025.findings-acl)
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| Challenge: | Existing research shows LLMs struggle with complex instructions involving multiple constraints. |
| Approach: | They propose a framework to divide complex instructions into single constraints and prepare appropriate tools to verify responses. |
| Outcome: | The proposed framework doubles Llama3.1-8B’s constraint adherence and triples Mistral-7B’ s performance. |
Image Corruption-Inspired Membership Inference Attacks against Large Vision-Language Models (2026.eacl-long)
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| Challenge: | Large vision-language models (LVLMs) are trained on large-scale datasets, which can pose privacy risks if training images contain sensitive information. |
| Approach: | They propose to detect whether a target image is used to train LVLMs by using image-text pairs and single-modality content to detect image-related data. |
| Outcome: | The proposed methods detect whether a target image is used to train the LVLM on large-scale datasets. |
Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation (2026.findings-eacl)
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Minhua Lin, Zhengzhang Chen, Yanchi Liu, Xujiang Zhao, Zongyu Wu, Junxiang Wang, Xiang Zhang, Suhang Wang, Haifeng Chen
| Challenge: | Time series data is ubiquitous across various domains, including manufacturing, finance, and healthcare. |
| Approach: | They propose a multi-agent system to generate general and domain-specific annotations for time series data. |
| Outcome: | The proposed system outperforms existing methods on synthetic and real-world datasets. |
Universal Prompt Optimizer for Safe Text-to-Image Generation (2024.naacl-long)
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| Challenge: | Existing studies based on image checker, model fine-tuning and embedding blocking are impractical in real-world applications. |
| Approach: | They propose a novel reward function measuring toxicity and text alignment of generated images and train the optimizer through Proximal Policy Optimization. |
| Outcome: | The proposed model reduces the likelihood of various models in generating inappropriate images, with no significant impact on text alignment. |