ArticleBriefings in bioinformatics2025
Should LLMs be over empowered for high-stake regulatory research?
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Abstract
This letter critically evaluates the feasibility of implementing open-source large language models in regulatory research, building upon the recent study on zero-shot and few-shot learning approaches for regulatory tasks. While the study demonstrates that models like Flan-T5 can effectively extract pharmacokinetic drug-drug interactions and intrinsic factors from Food and Drug Administration (US) drug labels with high precision, it also highlights significant challenges, including computational constraints, performance variability, prompt sensitivity, and the risk of misclassification. To address these issues, this letter discusses intuitive ways for mitigating these limitations.
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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.