Deyuan Li — accepted AI-conference papers

2024

M3SciQA: A Multi-Modal Multi-Document Scientific QA Benchmark for Evaluating Foundation Models

Chuhan Li, Ziyao Shangguan, Yilun Zhao, Deyuan Li, Yixin Liu, Arman Cohan

EMNLP 2024finding

Existing evaluation benchmarks for foundation models in understanding scientific literature predominantly focus on single-document, text-only tasks. Such benchmarks often do not adequately represent the complexity of research workflows, which typically also involve interpreting non-textual data, suc…

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2022

Bridging the Gap: Unifying the Training and Evaluation of Neural Network Binary Classifiers

Nathan Tsoi, Kate Candon, Deyuan Li, Yofti Milkessa, Marynel Vazquez

NeurIPS 2022accept

While neural network binary classifiers are often evaluated on metrics such as Accuracy and $F_1$-Score, they are commonly trained with a cross-entropy objective. How can this training-evaluation gap be addressed? While specific techniques have been adopted to optimize certain confusion matrix based…

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