Papers by Dongfu Jiang
ACECODER: Acing Coder RL via Automated Test-Case Synthesis (2025.acl-long)
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| Challenge: | Recent coder models have been driven by supervised fine-tuning, but the potential of reinforcement learning remains unexplored due to the lack of reliable reward data/model in the code domain. |
| Approach: | They propose a pipeline that generates extensive test-case pairs from existing code data and constructs preference pairs based on pass rates over sampled programs. |
| Outcome: | The proposed pipeline generates extensive (question, test-cases) pairs from existing code data and trains them with Bradley-Terry loss. |
VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation (2024.emnlp-main)
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Xuan He, Dongfu Jiang, Ge Zhang, Max Ku, Achint Soni, Sherman Siu, Haonan Chen, Abhranil Chandra, Ziyan Jiang, Aaran Arulraj, Kai Wang, Quy Do, Yuansheng Ni, Bohan Lyu, Yaswanth Narsupalli, Rongqi Fan, Zhiheng Lyu, Bill Yuchen Lin, Wenhu Chen
| Challenge: | Existing video metrics are lagging behind in providing reliable scores over generated videos due to lack of large-scale human-annotated dataset. |
| Approach: | They propose to use VideoFeedback to train a human-annotated multi-aspect score over 37.6K synthesized videos from 11 existing video generative models. |
| Outcome: | The proposed model outperforms the prior best metrics by 50 points in the test. |
LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion (2023.acl-long)
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| Challenge: | a recent study shows that open-source large language models (LLMs) exhibit diverse strengths and weaknesses due to variations in their architectures and training data. |
| Approach: | They propose a framework that leverages the diverse strengths of open-source large language models. |
| Outcome: | The proposed framework outperforms individual LLMs and baseline methods across various metrics, establishing a substantial performance gap. |
VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation (2024.acl-long)
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| Challenge: | Existing metrics for conditional image generation are opaque and lack explainability . evaluators of these metrics have limited ability to evaluate image synthesis tasks . |
| Approach: | They propose a Visual Instruction-guided Explainable metric for evaluating conditional image models. |
| Outcome: | The proposed model achieves a high Spearman correlation with human evaluations, but is weaker than GPT-4o and GPT-v in evaluating synthetic images. |
ReviewGrounder: Improving Review Substantiveness with Rubric-Guided, Tool-Integrated Agents (2026.acl-long)
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Zhuofeng Li, Yi Lu, Dongfu Jiang, Haoxiang Zhang, Yuyang Bai, Chuan Li, Yu Wang, Shuiwang Ji, Jianwen Xie, Yu Zhang
| Challenge: | Rapid rise in AI conference submissions has driven increasing exploration of large language models (LLMs) for peer review support. |
| Approach: | They propose a peer review benchmarking tool based on paper-specific rubrics and a rubric-guided framework that decomposes reviewing into drafting and grounding stages. |
| Outcome: | The proposed framework outperforms baselines with stronger/larger backbones in both alignment with human judgments and rubric-based review quality across 8 dimensions. |