ArticlePatterns (New York, N.Y.)2023
Fine-tuning large neural language models for biomedical natural language processing.
Article in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
56 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Data extraction methods for systematic review (semi)automation: Update of a living systematic review.F1000Research · 2021Pooled 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
- A Natural Language Processing Framework for Structuring and Visualizing Clinical Trial Eligibility Criteria at Scale: Protocol for a Quantitative Study.JMIR research protocols · 2026Article
- MnMR-GenA: a morphological recombination genetic algorithm for jailbreak attacks in low-resource language.Scientific reports · 2026Article
- Cosynllm: predicting drug combination synergy with LLM-generated descriptions.Journal of cheminformatics · 2026Article
- Transforming vacant rural school buildings into age-friendly facilities under China's aging and low-fertility context: a natural language processing-based evaluation framework.BMC geriatrics · 2026Article
- Scoring Physician Risk Communication in Prostate Cancer Using Large Language Models.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Fine-tuning and structured prompting strategies for question answering over full-text biomedical research articles.PloS one · 2026Article
- An efficient strategy for fine-tuning large language models.Frontiers in artificial intelligence · 2026Article
- A FAIR gold-standard benchmark for geographic entity recognition in cancer genomics literature for biomedical NLP.Bioinformatics advances · 2026Article
- GenBank2PubMed: bridging viral genomic data and the scientific literature with AI-assisted curation.Scientific reports · 2025Article
- Evaluating Language Models for Biomedical Fact-Checking: A Benchmark Dataset for Cancer Variant Interpretation Verification.bioRxiv : the preprint server for biology · 2025Article
- Mining the neuroimaging literature.eLife · 2025Article
- Reduction of supervision for biomedical knowledge discovery.BMC bioinformatics · 2025Article
- Mind the Gap: Does Brain Age Improve Alzheimer's Disease Prediction?Human brain mapping · 2025Article
- Scoring Physician Risk Communication in Prostate Cancer Using Large Language Models.medRxiv : the preprint server for health sciences · 2025Article
- Detection of patient metadata in published articles for genomic epidemiology using machine learning and large language models.medRxiv : the preprint server for health sciences · 2025Article
- Article
- Benchmarking large language models for biomedical natural language processing applications and recommendations.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large neural language models have transformed modern natural language processing (NLP) applications. However, fine-tuning such models for specific tasks remains challenging as model size increases, especially with small labeled datasets, which are common in biomedical NLP. We conduct a systematic study on fine-tuning stability in biomedical NLP. We show that fine-tuning performance may be sensitive to pretraining settings and conduct an exploration of techniques for addressing fine-tuning instability. We show that these techniques can substantially improve fine-tuning performance for low-resource biomedical NLP applications. Specifically, freezing lower layers is helpful for standard BERT-
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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.