MedINST: Meta Dataset of Biomedical Instructions
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.
671a296241f8a9c7fec7defd
Wenhan Han
671a296241f8a9c7fec7defe
Meng Fang
671a296241f8a9c7fec7deff
Zihan Zhang
671a296241f8a9c7fec7df00
Yu Yin
671a296241f8a9c7fec7df01
Zirui Song
admin_assigned
2024-10-24T12:39:39.556Z
65407ba7a38390065750233f
https://cdn-avatars.huggingface.co/v1/production/uploads/65407ba7a38390065750233f/1_IPMZbk-S9u2t18PQgMp.jpeg
Zirui Song
user
Ziruibest
671a296241f8a9c7fec7df02
Ling Chen
671a296241f8a9c7fec7df03
Mykola Pechenizkiy
671a296241f8a9c7fec7df04
Qingyu Chen
claimed_verified
2024-11-03T14:23:54.024Z
65a57702810edff295a472d8
/avatars/ebc91907617c034326dc14e3692890ce.svg
qc15
user
QC15
MedINST: Meta Dataset of Biomedical Instructions
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.
MedINST, a comprehensive biomedical instruction dataset, enhances LLMs' generalization across various NLP tasks through fine-tuning and benchmarking.
large language model (LLM) medical analysis MedINST biomedical NLP tasks meta-dataset MedINST32 cross-task generalization