{"id":"https://openalex.org/W7117462375","doi":"https://doi.org/10.1145/3714394.3756275","title":"Evaluating Large Language Models in Traditional Chinese Medicine with Enhanced Misinformation Detection","display_name":"Evaluating Large Language Models in Traditional Chinese Medicine with Enhanced Misinformation Detection","publication_year":2025,"publication_date":"2025-10-12","ids":{"openalex":"https://openalex.org/W7117462375","doi":"https://doi.org/10.1145/3714394.3756275"},"language":null,"primary_location":{"id":"doi:10.1145/3714394.3756275","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3714394.3756275","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3714394.3756275","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102850818","display_name":"Yawen Zheng","orcid":"https://orcid.org/0009-0005-4225-6965"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yawen Zheng","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085892006","display_name":"Jinliang Yuan","orcid":"https://orcid.org/0000-0002-2141-1496"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinliang Yuan","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121482066","display_name":"Li Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Liu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036391537","display_name":"Wufan Zhao","orcid":"https://orcid.org/0000-0002-0265-3465"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]},{"id":"https://openalex.org/I3129498420","display_name":"National University of Architecture and Construction of Armenia","ror":"https://ror.org/002s77n28","country_code":"AM","type":"education","lineage":["https://openalex.org/I3129498420"]}],"countries":["AM","US"],"is_corresponding":false,"raw_author_name":"Wenkai Zhao","raw_affiliation_strings":["Cornell University, Ithaca, Armenia"],"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, Armenia","institution_ids":["https://openalex.org/I205783295","https://openalex.org/I3129498420"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5120313172","display_name":"Zhenyu Wang","orcid":"https://orcid.org/0009-0001-9415-1859"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenyu Wang","raw_affiliation_strings":["Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5102850818"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.76892856,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1358","last_page":"1363"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12647","display_name":"Traditional Chinese Medicine Studies","score":0.5577999949455261,"subfield":{"id":"https://openalex.org/subfields/2707","display_name":"Complementary and alternative medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T12647","display_name":"Traditional Chinese Medicine Studies","score":0.5577999949455261,"subfield":{"id":"https://openalex.org/subfields/2707","display_name":"Complementary and alternative medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11147","display_name":"Misinformation and Its Impacts","score":0.052799999713897705,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.03539999946951866,"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/misinformation","display_name":"Misinformation","score":0.7620999813079834},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.3822999894618988},{"id":"https://openalex.org/keywords/traditional-chinese-medicine","display_name":"Traditional Chinese medicine","score":0.3785000145435333},{"id":"https://openalex.org/keywords/evaluation-methods","display_name":"Evaluation methods","score":0.3138999938964844},{"id":"https://openalex.org/keywords/chinese-language","display_name":"Chinese language","score":0.3003000020980835}],"concepts":[{"id":"https://openalex.org/C2776990098","wikidata":"https://www.wikidata.org/wiki/Q13579947","display_name":"Misinformation","level":2,"score":0.7620999813079834},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6097999811172485},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4903999865055084},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4666999876499176},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3822999894618988},{"id":"https://openalex.org/C188947578","wikidata":"https://www.wikidata.org/wiki/Q200253","display_name":"Traditional Chinese medicine","level":3,"score":0.3785000145435333},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3718999922275543},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33559998869895935},{"id":"https://openalex.org/C3018395757","wikidata":"https://www.wikidata.org/wiki/Q1379672","display_name":"Evaluation methods","level":2,"score":0.3138999938964844},{"id":"https://openalex.org/C3018428822","wikidata":"https://www.wikidata.org/wiki/Q7850","display_name":"Chinese language","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C3020132585","wikidata":"https://www.wikidata.org/wiki/Q2671652","display_name":"Diagnostic accuracy","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C2779473830","wikidata":"https://www.wikidata.org/wiki/Q1540899","display_name":"MEDLINE","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.272599995136261},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.25699999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3714394.3756275","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3714394.3756275","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3714394.3756275","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3714394.3756275","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.6326858401298523}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W3162922479","https://openalex.org/W4393318031","https://openalex.org/W4399803256","https://openalex.org/W4404837143","https://openalex.org/W4408226143"],"related_works":[],"abstract_inverted_index":{"Current":[0],"medical":[1,86],"LLM":[2],"evaluation":[3,28,97],"prioritizes":[4],"accuracy":[5,92],"over":[6,37],"safety,":[7],"creating":[8],"a":[9],"''positive-negative":[10],"capability":[11],"divide''":[12],"where":[13],"models":[14,59],"excel":[15],"at":[16],"selecting":[17],"correct":[18],"answers":[19],"but":[20],"struggle":[21],"to":[22],"detect":[23],"misinformation.":[24],"We":[25],"develop":[26],"an":[27],"framework":[29],"using":[30],"Traditional":[31],"Chinese":[32],"Medicine":[33],"(TCM)":[34],"examinations,":[35],"transforming":[36],"3,000":[38],"questions":[39],"into":[40],"four":[41],"testing":[42],"paradigms:":[43],"Standard":[44],"multi-choice":[45],"questions,":[46],"Wrong":[47],"Options":[48],"Test,":[49,52],"Misleading":[50],"Guidance":[51],"and":[53,80,94],"Fabricated":[54],"Entity":[55],"Test.":[56],"Evaluating":[57],"six":[58],"reveals":[60],"striking":[61],"disparities:":[62],"while":[63],"some":[64],"achieve":[65],"clinical":[66],"thresholds":[67],"(>60%)":[68],"in":[69,76,83],"standard":[70,91],"evaluation,":[71],"all":[72],"experience":[73],"dramatic":[74],"degradation":[75],"hallucination":[77],"detection":[78],"(20-39%)":[79],"near-zero":[81],"performance":[82],"detecting":[84],"fabricated":[85],"concepts.":[87],"These":[88],"findings":[89],"challenge":[90],"metrics":[93],"establish":[95],"safety-prioritized":[96],"standards.":[98]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-12-29T00:00:00"}
