Papers by Deqing Fu
DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback (2025.naacl-long)
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Jiao Sun, Deqing Fu, Yushi Hu, Su Wang, Royi Rassin, Da-Cheng Juan, Dana Alon, Charles Herrmann, Sjoerd Van Steenkiste, Ranjay Krishna, Cyrus Rashtchian
| Challenge: | Text-to-Image models (T2I) still struggle to produce images that are both aesthetically pleasing and faithful to the user’s input text. |
| Approach: | They propose a training algorithm that trains T2I models to be faithful to the input text. |
| Outcome: | The proposed model improves both the semantic alignment and aesthetic appeal of two diffusion-based T2I models, evidenced by multiple benchmarks (+1.7% on TIFA, +2.9% on DSG1K, +3.4% on VILA aesthetic). |
SCENE: Self-Labeled Counterfactuals for Extrapolating to Negative Examples (2023.emnlp-main)
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| Challenge: | Manually collecting challenging negative examples can be costly and domain-specific. |
| Approach: | They propose a method that synthesizes negative examples from only positive ones using a mask infilling model. |
| Outcome: | The proposed method closes 69.6% of the performance gap on a dataset where half of the evaluation examples are unanswerable compared to a model trained on SQuAD 2.0. |
Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models (2026.acl-long)
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Woody Haosheng Gan, Deqing Fu, Julian Asilis, Ollie Liu, Vatsal Sharan, Robin Jia, Willie Neiswanger
| Challenge: | Steering methods have emerged as effective tools for guiding large language models’ behavior, yet multimodal large language model lacks comparable techniques due to architectural diversity and limited availability of multimodal steering vectors. |
| Approach: | They validate steering vectors derived solely from text-only LLM backbones and use a cross-modal transfer technique to reuse existing interpretability tools. |
| Outcome: | The proposed steering vectors can guide and enhance multimodal models using SPAR, Mean Shift, and Linear Probing. |
Distilling Script Knowledge from Large Language Models for Constrained Language Planning (2023.acl-long)
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Siyu Yuan, Jiangjie Chen, Ziquan Fu, Xuyang Ge, Soham Shah, Charles Jankowski, Yanghua Xiao, Deqing Yang
| Challenge: | Existing work exploits language models to plan for abstract goals of stereotypical activities, but leaves more specific goals with multi-facet constraints understudied. |
| Approach: | They propose an over-generate-then-filter approach to improve large language models on constrained language planning task by distilling a constrained script dataset. |
| Outcome: | The proposed approach improves the constrained language planning ability of large language models on constraint faithfulness and also in smaller LMs. |