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manufernandezbur/MoBiPlant

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manufernandezbur/MoBiPlantmanufernandezbur/Mo…cc-by-4.0: declared licencecc-by-4.0declared licenceREADME.md ↗no licence file: found at top levelno licence fi…found at top levelfile list ↗
manufernandezbur/MoBiPlantmanufernandezbu…cc-by-4.0: declared licencecc-by-4.0declared licenceREADME.md ↗no licence file: found at top levelno licence fi…found at top levelfile list ↗
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Citation text from the card

@article {Fernandez Burda2025.08.31.672925,
    author = {Fernandez Burda, Manuel and Ferrero, Lucia and Gaggion, Nicol{\'a}s and Fonouni-Farde, Camille and The MoBiPlant Consortium and Crespi, Mart{\'\i}n and Ariel, Federico and Ferrante, Enzo},
    title = {What Large Language Models Know About Plant Molecular Biology},
    elocation-id = {2025.08.31.672925},
    year = {2025},
    doi = {10.1101/2025.08.31.672925},
    publisher = {Cold Spring Harbor Laboratory},
    abstract = {Large language models (LLMs) are rapidly permeating scientific research, yet their capabilities in plant molecular biology remain largely uncharacterized. Here, we present MoBiPlant, the first comprehensive benchmark for evaluating LLMs in this domain, developed by a consortium of 112 plant scientists across 19 countries. MoBiPlant comprises 565 expert-curated multiple-choice questions and 1,075 synthetically generated questions, spanning core topics from gene regulation to plant-environment interactions. We benchmarked seven leading chat-based LLMs using both automated scoring and human evaluation of open-ended answers. Models performed well on multiple-choice tasks (exceeding 75% accuracy), although most of them exhibited a consistent bias towards option A. In contrast, expert reviews exposed persistent limitations, including factual misalignment, hallucinations, and low self-awareness. Critically, we found that model performance strongly correlated with the citation frequency of source literatur

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dataset_licence({ "id": "manufernandezbur/MoBiPlant" })
signal: "permissive"
stated_licence: "cc-by-4.0"
sources: 0