4 citations · 6 across the 3 of their papers we have counts for
4 papers
IAPT: Instruction-Aware Prompt Tuning for Large Language Models
Wei Zhu, Aaron Xuxiang Tian, Congrui Yin +3
Soft prompt tuning is a widely studied parameter-efficient fine-tuning method. However, it has a clear drawback: many soft tokens must be inserted into the input sequences to guara…
TCMBench: A Comprehensive Benchmark for Evaluating Large Language Models in Traditional Chinese Medicine
Wenjing Yue, Xiaoling Wang, Wei Zhu +5
Large language models (LLMs) have performed remarkably well in various natural language processing tasks by benchmarking, including in the Western medical domain. However, the prof…
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…
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…