10 papers
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
SciMDR: Advancing Scientific Multimodal Document Reasoning
Ziyu Chen, Yilun Zhao, Chengye Wang +3
Constructing scientific multimodal document reasoning datasets for foundation model training involves an inherent trade-off among scale, faithfulness, and realism. To address this…
TexOCR: Advancing Document OCR Models for Compilable Page-to-LaTeX Reconstruction
Chengye Wang, Lin Fu, Zexi Kuang +1
Existing document OCR largely targets plain text or Markdown, discarding the structural and executable properties that make LaTeX essential for scientific publishing. We study page…
Generative Model Unlearning: A Survey through Target Events, Unlearning Operators, and Evaluation Protocols
Xiaohua Feng, Jiaming Zhang, Fengyuan Yu +7
With the rapid advancement of generative models, privacy, copyright, safety, and reliability risks have attracted growing attention. To mitigate these risks, machine unlearning has…
AbGen: Evaluating Large Language Models in Ablation Study Design and Evaluation for Scientific Research
Yilun Zhao, Weiyuan Chen, Zhijian Xu +5
We introduce AbGen, the first benchmark designed to evaluate the capabilities of LLMs in designing ablation studies for scientific research. AbGen consists of 1,500 expert-annotate…
Can Multimodal Foundation Models Understand Schematic Diagrams? An Empirical Study on Information-Seeking QA over Scientific Papers
Yilun Zhao, Chengye Wang, Chuhan Li +1
This paper introduces MISS-QA, the first benchmark specifically designed to evaluate the ability of models to interpret schematic diagrams within scientific literature. MISS-QA com…