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Evaluating large language models for multilingual vulnerability detection at dual granularities

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Published in:EMSE
Format: Online Article RSS Article
Published: 2026
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discipline_display Technology & Engineering
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format Online Article
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institution FRELIP
journal_source_facet EMSE
publishDate 2026
publishDateSort 2026
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spellingShingle Evaluating large language models for multilingual vulnerability detection at dual granularities
Computer Software-Systems
Technology & Engineering — Computing
Technology & Engineering
sub_discipline_display Technology & Engineering — Computing
sub_discipline_facet Technology & Engineering — Computing
subject_display Computer Software-Systems
Technology & Engineering — Computing
Technology & Engineering
Computer Software-Systems
Technology & Engineering — Computing
Technology & Engineering
subject_facet Computer Software-Systems
Technology & Engineering — Computing
Technology & Engineering
title Evaluating large language models for multilingual vulnerability detection at dual granularities
title_auth Evaluating large language models for multilingual vulnerability detection at dual granularities
title_full Evaluating large language models for multilingual vulnerability detection at dual granularities
title_fullStr Evaluating large language models for multilingual vulnerability detection at dual granularities
title_full_unstemmed Evaluating large language models for multilingual vulnerability detection at dual granularities
title_short Evaluating large language models for multilingual vulnerability detection at dual granularities
title_sort evaluating large language models for multilingual vulnerability detection at dual granularities
topic Computer Software-Systems
Technology & Engineering — Computing
Technology & Engineering
url https://link.springer.com/article/10.1007/s10664-026-10832-4