MedINST: Meta Dataset of Biomedical Instructions
Authors: name: Wenhan Han, name: Meng Fang, name: Zihan Zhang, name: Yu Yin, name: Zirui Song, name: Ling Chen, name: Mykola Pechenizkiy, name: Qingyu Chen

The integration of large language model (LLM) techniques in the field of
medical analysis has brought about significant advancements, yet the scarcity
of large, diverse, and well-annotated datasets remains a major challenge.
Medical data and tasks, which vary in format, size, and other parameters,
require extensive preprocessing and standardization for effective use in
training LLMs. To address these challenges, we introduce MedINST, the Meta
Dataset of Biomedical Instructions, a novel multi-domain, multi-task
instructional meta-dataset. MedINST comprises 133 biomedical NLP tasks and over
7 million training samples, making it the most comprehensive biomedical
instruction dataset to date. Using MedINST as the meta dataset, we curate
MedINST32, a challenging benchmark with different task difficulties aiming to
evaluate LLMs' generalization ability. We fine-tune several LLMs on MedINST and
evaluate on MedINST32, showcasing enhanced cross-task generalization.