ArticleJournal of the American Medical Informatics Association : JAMIA2024
BioInstruct: instruction tuning of large language models for biomedical natural language processing.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Natural language generation in healthcare: A review of methods and applications.Journal of biomedical informatics · 2026Pooled it
- The Rise of Small Language Models in Healthcare: A Comprehensive Survey.Computer science review · 2026Article
- Generative large language models in medicine: a scoping review of recent methodological advances.npj health systems · 2026Review
- Article
- Domain-specific versus general large language models: a review and empirical benchmark in real medical texts.Scientific reports · 2026Review
- Article
- Reinforcement learning improves LLM accuracy and reasoning in disease classification from radiology reports.NPJ digital medicine · 2026Article
- MedQA-CS: Objective Structured Clinical Examination (OSCE)-Style Benchmark for Evaluating LLM Clinical Skills.Proceedings of the conference. Association for Computational Linguistics. European Chapter. Conference · 2026Article
- Fine-tuning and structured prompting strategies for question answering over full-text biomedical research articles.PloS one · 2026Article
- From Zero-Shot to Bedside: A Practical Playbook for Adapting Open-Source Large Language Models to Clinical Symptom Extraction.Proceedings of machine learning research · 2026Article
- Digital twin-supported behavioral intention in mothers of young children to prevent childhood obesity: a large language model-based intervention study.Frontiers in artificial intelligence · 2026Article
- A review for navigating the trade-offs: evaluating open-source and proprietary large language models for clinical and biomedical information extraction.Frontiers in digital health · 2026Review
- A roadmap for medical large language models: a review of foundations, applications, and challenges.Military Medical Research · 2026Review
- Leveraging ChatGPT and explainable AI for enhancing clinical decision support.Scientific reports · 2025Article
- DischargeSim: A Simulation Benchmark for Educational Doctor-Patient Communication at Discharge.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2025Article
- Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2025Article
- MedCOD: Enhancing English-to-Spanish Medical Translation of Large Language Models Using Enriched Chain-of-Dictionary Framework.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2025Article
- Article
- Out-of-the-box bioinformatics capabilities of large language models (LLMs).bioRxiv : the preprint server for biology · 2025Article
- The Development Landscape of Large Language Models for Biomedical Applications.Annual review of biomedical data science · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectivesTo enhance the performance of large language models (LLMs) in biomedical natural language processing (BioNLP) by introducing a domain-specific instruction dataset and examining its impact when combined with multi-task learning principles. MATERIALS AND
methodsWe created the BioInstruct, comprising 25 005 instructions to instruction-tune LLMs (LLaMA 1 and 2, 7B and 13B version). The instructions were created by prompting the GPT-4 language model with 3-seed samples randomly drawn from an 80 human curated instructions. We employed Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning. We then evaluated these instruction-tuned LLMs on several BioNLP tasks, which can be grouped into 3 major categories: question answering (QA), information extraction (IE), and text generation (GEN). We also examined whether categories (eg, QA, IE, and generation) of instructions impact model performance. RESULTS AND DISCUSSION: Comparing with LLMs without instruction-tuned, our instruction-tuned LLMs demonstrated marked performance gains: 17.3% in QA on average accuracy metric, 5.7% in IE on average F1 metric, and 96% in Generation tasks on average GPT-4 score metric. Our 7B-parameter instruction-tuned LLaMA 1 model was competitive or even surpassed other LLMs in the biomedical domain that were also fine-tuned from LLaMA 1 with vast domain-specific data or a variety of tasks. Our results also show that the performance gain is significantly higher when instruction fine-tuning is conducted with closely related tasks. Our findings align with the observations of multi-task learning, suggesting the synergies between 2 tasks.
conclusionThe BioInstruct dataset serves as a valuable resource and instruction tuned LLMs lead to the best performing BioNLP applications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.