Taiyi: a bilingual fine-tuned large language model for diverse biomedical tasks

Authors:
raw_author_name: Ling Luo
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology , Dalian 116024, China
raw_author_name: Jinzhong Ning
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology , Dalian 116024, China
raw_author_name: Zhijun Wang
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology , Dalian 116024, China
raw_author_name: Zeyuan Ding
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology , Dalian 116024, China
raw_author_name: Peng Chen
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology , Dalian 116024, China
raw_author_name: Weiru Fu
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology , Dalian 116024, China
raw_author_name: Guangtao Xu
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology , Dalian 116024, China
raw_author_name: Yunzhi Qiu
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology , Dalian 116024, China
raw_author_name: Dinghao Pan
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology , Dalian 116024, China
raw_author_name: Jiru Li
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology , Dalian 116024, China
raw_author_name: Hao Li
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology , Dalian 116024, China
raw_author_name: Wenduo Feng
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology , Dalian 116024, China
raw_author_name: Senbo Tu
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology , Dalian 116024, China
raw_author_name: Yuqi Liu
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology , Dalian 116024, China
raw_author_name: Zhihao Yang
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology , Dalian 116024, China
raw_author_name: Jian Wang
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology , Dalian 116024, China
raw_author_name: Yuanyuan Sun
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology , Dalian 116024, China
raw_author_name: Hongfei Lin
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology , Dalian 116024, China

Abstract: Most existing fine-tuned biomedical large language models (LLMs) focus on enhancing performance in monolingual biomedical question answering and conversation tasks. To investigate the effectiveness of the fine-tuned LLMs on diverse biomedical natural language processing (NLP) tasks in different languages, we present Taiyi, a bilingual fine-tuned LLM for diverse biomedical NLP tasks.We first curated a comprehensive collection of 140 existing biomedical text mining datasets (102 English and 38 Chinese datasets) across over 10 task types. Subsequently, these corpora were converted to the instruction data used to fine-tune the general LLM. During the supervised fine-tuning phase, a 2-stage strategy is proposed to optimize the model performance across various tasks.Experimental results on 13 test sets, which include named entity recognition, relation extraction, text classification, and question answering tasks, demonstrate that Taiyi achieves superior performance compared to general LLMs. The case study involving additional biomedical NLP tasks further shows Taiyi's considerable potential for bilingual biomedical multitasking.Leveraging rich high-quality biomedical corpora and developing effective fine-tuning strategies can significantly improve the performance of LLMs within the biomedical domain. Taiyi shows the bilingual multitasking capability through supervised fine-tuning. However, those tasks such as information extraction that are not generation tasks in nature remain challenging for LLM-based generative approaches, and they still underperform the conventional discriminative approaches using smaller language models.