{"id":"https://openalex.org/W7134078533","doi":"https://doi.org/10.48550/arxiv.2603.05308","title":"Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution","display_name":"Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution","publication_year":2026,"publication_date":"2026-03-05","ids":{"openalex":"https://openalex.org/W7134078533","doi":"https://doi.org/10.48550/arxiv.2603.05308"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.05308","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100611571","display_name":"Qiao Jin","orcid":"https://orcid.org/0000-0002-1268-7239"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Qiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126190822","display_name":"Yin Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Yin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013808537","display_name":"Lauren He","orcid":"https://orcid.org/0000-0003-1834-8825"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Lauren","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128257073","display_name":"Yifan Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Xiong, Guangzhi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiong, Guangzhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128232496","display_name":"Zhizheng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zhizheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114618053","display_name":"Nicholas Wan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wan, Nicholas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128236560","display_name":"Joey Chan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chan, Joey","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031842571","display_name":"Donald C. Comeau","orcid":"https://orcid.org/0000-0002-9373-530X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Comeau, Donald C.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044947146","display_name":"Robert Leaman","orcid":"https://orcid.org/0000-0003-3296-5766"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Leaman, Robert","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047939366","display_name":"Charalampos S. Floudas","orcid":"https://orcid.org/0000-0002-0020-237X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Floudas, Charalampos S.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128237940","display_name":"Aidong Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Aidong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128267039","display_name":"Michael F. Chiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chiang, Michael F.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128247976","display_name":"Yifan Peng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128239192","display_name":"Zhiyong Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Zhiyong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.25440933,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.2176000028848648,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.2176000028848648,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.17350000143051147,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.1386999934911728,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6013000011444092},{"id":"https://openalex.org/keywords/assertion","display_name":"Assertion","score":0.5715000033378601},{"id":"https://openalex.org/keywords/citation","display_name":"Citation","score":0.5360000133514404},{"id":"https://openalex.org/keywords/frontier","display_name":"Frontier","score":0.48100000619888306},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4805000126361847},{"id":"https://openalex.org/keywords/attribution","display_name":"Attribution","score":0.46369999647140503},{"id":"https://openalex.org/keywords/blame","display_name":"Blame","score":0.3767000138759613}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6894999742507935},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6013000011444092},{"id":"https://openalex.org/C40422974","wikidata":"https://www.wikidata.org/wiki/Q741248","display_name":"Assertion","level":2,"score":0.5715000033378601},{"id":"https://openalex.org/C2778805511","wikidata":"https://www.wikidata.org/wiki/Q1713","display_name":"Citation","level":2,"score":0.5360000133514404},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.5313000082969666},{"id":"https://openalex.org/C2778571376","wikidata":"https://www.wikidata.org/wiki/Q1355821","display_name":"Frontier","level":2,"score":0.48100000619888306},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4805000126361847},{"id":"https://openalex.org/C143299363","wikidata":"https://www.wikidata.org/wiki/Q900584","display_name":"Attribution","level":2,"score":0.46369999647140503},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4544000029563904},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.447299987077713},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.382999986410141},{"id":"https://openalex.org/C2781466463","wikidata":"https://www.wikidata.org/wiki/Q621695","display_name":"Blame","level":2,"score":0.3767000138759613},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.3571000099182129},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.35420000553131104},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3449999988079071},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.25369998812675476}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.05308","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.05308","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.05308","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2603.05308","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Assessing":[0],"whether":[1],"an":[2,5,193],"article":[3],"supports":[4],"assertion":[6],"is":[7,215],"essential":[8],"for":[9,109,202],"hallucination":[10,151],"detection":[11],"and":[12,142,195,204,211],"claim":[13],"verification.":[14],"While":[15],"large":[16],"language":[17,57],"models":[18,32,58,82],"(LLMs)":[19],"have":[20],"the":[21,135],"potential":[22],"to":[23,40,78,99,115,154,186,199],"automate":[24],"this":[25,72],"task,":[26],"achieving":[27],"strong":[28],"performance":[29],"requires":[30],"frontier":[31,100,200],"such":[33,102],"as":[34,103],"GPT-5":[35,145],"that":[36,122,134,164,182],"are":[37,183],"prohibitively":[38],"expensive":[39],"deploy":[41],"at":[42,188,217],"scale.":[43,189],"To":[44],"efficiently":[45],"perform":[46],"biomedical":[47,85,208],"evidence":[48,170,209],"attribution,":[49],"we":[50,157],"present":[51,158],"Med-V1,":[52],"a":[53,89,117,159],"family":[54],"of":[55],"small":[56],"with":[59,106,144],"only":[60],"three":[61],"billion":[62],"parameters.":[63],"Trained":[64],"on":[65,83],"high-quality":[66,107],"synthetic":[67],"data":[68],"newly":[69],"developed":[70],"in":[71,125,172,207],"study,":[73],"Med-V1":[74,96,114,165,191,214],"substantially":[75],"outperforms":[76],"(+27.0%":[77],"+71.3%)":[79],"its":[80,93,110],"base":[81],"five":[84],"benchmarks":[86],"unified":[87],"into":[88],"verification":[90,212],"format.":[91],"Despite":[92],"smaller":[94],"size,":[95],"performs":[97],"comparably":[98],"LLMs":[101,201],"GPT-5,":[104],"along":[105],"explanations":[108],"predictions.":[111],"We":[112],"use":[113,119,161],"conduct":[116],"first-of-its-kind":[118],"case":[120,162],"study":[121],"quantifies":[123],"hallucinations":[124],"LLM-generated":[126],"answers":[127],"under":[128],"different":[129],"citation":[130,140],"instructions.":[131],"Results":[132],"show":[133],"format":[136],"instruction":[137],"strongly":[138],"affects":[139],"validity":[141],"hallucination,":[143],"generating":[146],"more":[147],"claims":[148],"but":[149],"exhibiting":[150],"rates":[152],"similar":[153],"GPT-4o.":[155],"Additionally,":[156],"second":[160],"showing":[163],"can":[166],"automatically":[167],"identify":[168,187],"high-stakes":[169],"misattributions":[171],"clinical":[173],"practice":[174],"guidelines,":[175],"revealing":[176],"potentially":[177],"negative":[178],"public":[179],"health":[180],"impacts":[181],"otherwise":[184],"challenging":[185],"Overall,":[190],"provides":[192],"efficient":[194],"accurate":[196],"lightweight":[197],"alternative":[198],"practical":[203],"real-world":[205],"applications":[206],"attribution":[210],"tasks.":[213],"available":[216],"https://github.com/ncbi-nlp/Med-V1.":[218]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-07T00:00:00"}