<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>63(3)</volume><submitter>Perkins G</submitter><journal>Journal of clinical microbiology</journal><pagination>e0162424</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11898693</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Retrieval-augmented generation salvages poor performance from large language models in answering microbiology-specific multiple-choice questions.</pubmed_title><pmcid>PMC11898693</pmcid><pubmed_authors>Perkins G</pubmed_authors><pubmed_authors>Spies NC</pubmed_authors><pubmed_authors>Anderson NW</pubmed_authors></additional><is_claimable>false</is_claimable><name>Retrieval-augmented generation salvages poor performance from large language models in answering microbiology-specific multiple-choice questions.</name><description/><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Mar</publication><modification>2025-04-04T01:27:53.837Z</modification><creation>2025-04-04T01:27:53.837Z</creation></dates><accession>S-EPMC11898693</accession><cross_references><pubmed>39932275</pubmed><doi>10.1128/jcm.01624-24</doi></cross_references></HashMap>