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