Papers by Yiling Ma
Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future (2026.acl-long)
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Sihong Wu, Owen Jiang, Yilun Zhao, Tiansheng Hu, Yiling Ma, Kaiyan Zhang, Manasi Patwardhan, Arman Cohan
| Challenge: | Recent advances in large language models (LLMs) motivated methods that assist or automate different stages of peer review pipeline. |
| Approach: | They synthesize techniques to enhance peer review generation and after-review tasks aligned to reviews. |
| Outcome: | The proposed methods improve the peer review process by fine-tuning strategies, agent-based systems, and emerging paradigms. |
AEGIS: A Holistic Benchmark for Evaluating Forensic Analysis of AI-Generated Academic Images (2026.acl-long)
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Bo Zhang, Tzu-Yen Ma, Zichen Tang, Junpeng Ding, Zirui Wang, Yizhuo Zhao, Peilin Gao, Zijie Xi, Zixin Ding, Haiyang Sun, Haocheng Gao, Yuan Liu, Liangjia Wang, Yiling Huang, Yujie Wang, Yuyue Zhang, Ronghui Xi, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Haihong E
| Challenge: | AEGIS examines whether current models can effectively audit AI-generated images in academic papers. |
| Approach: | They propose a holistic benchmark for forensic analysis of AI-Generated academic ImageS that reveals limitations in academic image forensics. |
| Outcome: | AEGIS compared with existing benchmarks on seven academic categories and features key advances in forensic analysis. |
RbtAct: Rebuttal as Supervision for Actionable Review Feedback Generation (2026.findings-acl)
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| Challenge: | Prior studies show that large language models can draft fluent reviews but they miss specific issues, show shallow analysis, and produce generic phrasing. |
| Approach: | They propose a task that targets actionable review feedback generation and places existing peer review rebuttal at the center of learning. |
| Outcome: | The proposed model improves on a large dataset that maps review segments to rebuttal segments that address them, with perspective labels and impact categories that order author uptake. |