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Yingya Li

5 accepted papers

2025

WorldMedQA-V: a multilingual, multimodal medical examination dataset for multimodal language models evaluation

João Matos, Shan Chen, Siena Kathleen V. Placino, Yingya Li, Juan Carlos Climent Pardo, Daphna Idan +10
NAACL 2025findings

Multimodal/vision language models (VLMs) are increasingly being deployed in healthcare settings worldwide, necessitating robust benchmarks to ensure their safety, efficacy, and fairness. Multiple-choice question and answer (QA) datasets derived from national medical examinations have long served as…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗
2023

Measuring Pointwise $\mathcal{V}$-Usable Information In-Context-ly

Sheng Lu, Shan Chen, Yingya Li, Danielle Bitterman, Guergana K Savova, Iryna Gurevych
EMNLP 2023long findings

In-context learning (ICL) is a new learning paradigm that has gained popularity along with the development of large language models. In this work, we adapt a recently proposed hardness metric, pointwise $\mathcal{V}$-usable information (PVI), to an in-context version (in-context PVI). Compared to th…

Cited by 0Source ↗code ↗Scholar ↗
2023

Two-Stage Fine-Tuning for Improved Bias and Variance for Large Pretrained Language Models

Lijing Wang, Yingya Li, Timothy Miller, Steven Bethard, Guergana Savova
ACL 2023long

The bias-variance tradeoff is the idea that learning methods need to balance model complexity with data size to minimize both under-fitting and over-fitting. Recent empirical work and theoretical analysis with over-parameterized neural networks challenges the classic bias-variance trade-off notion s…

Cited by 6Source ↗PDF ↗code ↗Scholar ↗