mlsanityData to 14 Oct 2026

MedINST: Meta Dataset of Biomedical Instructions

arXiv 2410.13458 ↗First posted 17 Oct 2024cs.CLcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 32 of its 61 references that are in the map. First posted on arXiv 17 Oct 2024.

Builds on

32 of 61 references.

References · 32

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20 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 4.

7 papers.

  1. 2025ManipLVM-R1: Reinforcement Learning for Reasoning in Embodied Manipulation with Large Vision-Language Modelscited by 27
  2. 2025Audio Jailbreak: An Open Comprehensive Benchmark for Jailbreaking Large Audio-Language Modelscited by 21
  3. 2026PhantomBench: Benchmarking the Non-existential Threat of Language Modelscited by 2
  4. 2025MedPath: Multi-Domain Cross-Vocabulary Hierarchical Paths for Biomedical Entity Linkingcited by 1
  5. 2026Toward Federated Large Language Models in Medicine: A Parameter-Efficient Framework for Privacy-Preserving, Multi-Institutional Adaptationcited by 1
  6. 2026On the Utility and Factual Reliability of Pruned Mixture-of-Experts Models in the Biomedical Domaincited by 1
  7. 2026A Dataset of Robot-Patient and Doctor-Patient Medical Dialogues for Spoken Language Processing Taskscited by 0