March 15, 2024, 4:48 a.m. | Li Yizhen, Huang Shaohan, Qi Jiaxing, Quan Lei, Han Dongran, Luan Zhongzhi

cs.CL updates on arXiv.org arxiv.org

arXiv:2403.09164v1 Announce Type: new
Abstract: No previous work has studied the performance of Large Language Models (LLMs) in the context of Traditional Chinese Medicine (TCM), an essential and distinct branch of medical knowledge with a rich history. To bridge this gap, we present a TCM question dataset named TCM-QA, which comprises three question types: single choice, multiple choice, and true or false, to examine the LLM's capacity for knowledge recall and comprehensive reasoning within the TCM domain. In our study, …

abstract arxiv bridge chatgpt chinese context cs.cl dataset gap history knowledge language language models large language large language models llms medical medicine performance question stat.ap type work

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Associate Data Engineer

@ Nominet | Oxford/ Hybrid, GB

Data Science Senior Associate

@ JPMorgan Chase & Co. | Bengaluru, Karnataka, India