most citedPromptCBLUE: A Chinese Prompt Tuning Benchmark for the Medical Domain

4 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024

Toward Robust Multimodal Learning using Multimodal Foundational Models

Xianbing Zhao, Soujanya Poria, Xuejiao Li +2

Existing multimodal sentiment analysis tasks are highly rely on the assumption that the training and test sets are complete multimodal data, while this assumption can be difficult…

cs.CL20231 cited

Overview of the PromptCBLUE Shared Task in CHIP2023

Wei Zhu, Xiaoling Wang, Mosha Chen +1

This paper presents an overview of the PromptCBLUE shared task (http://cips-chip.org.cn/2023/eval1) held in the CHIP-2023 Conference. This shared task reformualtes the CBLUE benchm…

cs.CL20234 cited

PromptCBLUE: A Chinese Prompt Tuning Benchmark for the Medical Domain

Wei Zhu, Xiaoling Wang, Huanran Zheng +2

Biomedical language understanding benchmarks are the driving forces for artificial intelligence applications with large language model (LLM) back-ends. However, most current benchm…

cs.LG20231 cited

SHAPE: A Sample-adaptive Hierarchical Prediction Network for Medication Recommendation

Sicen Liu, Xiaolong Wang, JIngcheng Du +6

Effectively medication recommendation with complex multimorbidity conditions is a critical task in healthcare. Most existing works predicted medications based on longitudinal recor…

cs.CL2023

Revisiting Event Argument Extraction: Can EAE Models Learn Better When Being Aware of Event Co-occurrences?

Yuxin He, Jingyue Hu, Buzhou Tang

Event co-occurrences have been proved effective for event extraction (EE) in previous studies, but have not been considered for event argument extraction (EAE) recently. In this pa…