{"id":"https://openalex.org/W7161537171","doi":"https://doi.org/10.48550/arxiv.2605.15635","title":"Evaluating Chinese Ambiguity Understanding in Large Language Models","display_name":"Evaluating Chinese Ambiguity Understanding in Large Language Models","publication_year":2026,"publication_date":"2026-05-15","ids":{"openalex":"https://openalex.org/W7161537171","doi":"https://doi.org/10.48550/arxiv.2605.15635"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.15635","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.15635","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.15635","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114988522","display_name":"Junwen Mo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mo, Junwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101310368","display_name":"Yuanzhi Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Yuanzhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136452171","display_name":"Yifang Xue","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xue, Yifang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136384905","display_name":"Ke Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Ke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136431993","display_name":"Hideki Nakayama","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nakayama, Hideki","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":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.3601999878883362,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.3601999878883362,"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/T10028","display_name":"Topic Modeling","score":0.30250000953674316,"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/T13910","display_name":"Computational and Text Analysis Methods","score":0.08609999716281891,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.9513000249862671},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4846000075340271},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4690000116825104},{"id":"https://openalex.org/keywords/ambiguity-aversion","display_name":"Ambiguity aversion","score":0.35010001063346863},{"id":"https://openalex.org/keywords/ambiguity-resolution","display_name":"Ambiguity resolution","score":0.3452000021934509},{"id":"https://openalex.org/keywords/computational-linguistics","display_name":"Computational linguistics","score":0.3165999948978424},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.3107999861240387}],"concepts":[{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.9513000249862671},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5928000211715698},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4846000075340271},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4690000116825104},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43779999017715454},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4271000027656555},{"id":"https://openalex.org/C127447990","wikidata":"https://www.wikidata.org/wiki/Q4741446","display_name":"Ambiguity aversion","level":3,"score":0.35010001063346863},{"id":"https://openalex.org/C2777559092","wikidata":"https://www.wikidata.org/wiki/Q4741445","display_name":"Ambiguity resolution","level":4,"score":0.3452000021934509},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.3212999999523163},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.3086000084877014},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C197129107","wikidata":"https://www.wikidata.org/wiki/Q1921621","display_name":"Merge (version control)","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C169087156","wikidata":"https://www.wikidata.org/wiki/Q2131593","display_name":"Framing (construction)","level":2,"score":0.25600001215934753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.15635","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.15635","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.15635","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.15635","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"No poverty","score":0.430569052696228,"id":"https://metadata.un.org/sdg/1"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Linguistic":[0],"ambiguity":[1,27,56,91,107,156,162,170],"is":[2,50],"critical":[3],"to":[4,23,46],"the":[5,51],"robustness":[6],"of":[7,98],"Large":[8],"Language":[9],"Models":[10],"(LLMs),":[11],"yet":[12],"existing":[13],"research":[14,171],"focuses":[15],"mostly":[16],"on":[17],"English,":[18],"with":[19,90,115],"limited":[20],"attention":[21],"devoted":[22],"Chinese.":[24],"Existing":[25],"Chinese":[26,55,155,169],"datasets":[28],"(e.g.,":[29],"CHAmbi)":[30],"suffer":[31],"from":[32],"poor":[33],"scalability.":[34],"Guided":[35],"by":[36,94],"Potential":[37],"Ambiguity":[38],"(PA)":[39],"Theory,":[40],"we":[41,85],"design":[42],"a":[43,143,151,165],"semi-automatic":[44],"pipeline":[45],"construct":[47],"CHA-Gen.":[48],"It":[49],"first":[52],"PA":[53],"Theory-grounded":[54],"dataset,":[57],"which":[58],"comprises":[59],"5,712":[60],"sentences":[61],"(2,414":[62],"ambiguous,":[63],"3,298":[64],"unambiguous)":[65],"across":[66],"18":[67],"potential":[68],"ambiguous":[69,123],"structures.":[70],"Evaluating":[71],"LLMs":[72,88],"(e.g.":[73],"Gemma":[74],"3,":[75],"Qwen":[76],"2.5/3":[77],"series)":[78],"via":[79],"direct":[80],"querying":[81],"and":[82,110,158],"machine":[83],"translation,":[84],"find":[86],"that":[87,140],"struggle":[89],"detection":[92],"(improved":[93],"CoT":[95,100],"prompting).":[96],"Analysis":[97],"Qwen3-32B's":[99],"rationales":[101],"reveals":[102],"three":[103],"common":[104],"failure":[105],"modes:":[106],"blindness,":[108],"misattribution,":[109],"premature":[111],"resolution.":[112],"Uncertainty":[113],"quantification":[114],"semantic":[116,135],"entropy":[117],"metric":[118],"shows":[119],"higher":[120],"uncertainty":[121],"for":[122,154,167],"sentences.":[124],"Moreover,":[125],"instruction":[126],"tuning":[127],"induces":[128],"overconfidence,":[129],"whereas":[130],"Base":[131],"models":[132,141],"better":[133],"capture":[134],"diversity.":[136],"We":[137],"further":[138],"observe":[139],"exhibit":[142],"bias":[144],"toward":[145],"dominant":[146],"interpretations.":[147],"Our":[148],"work":[149],"provides":[150],"scalable":[152],"approach":[153],"corpus":[157],"insights":[159],"into":[160],"LLMs'":[161],"handling,":[163],"laying":[164],"foundation":[166],"enhancing":[168],"in":[172],"LLMs.":[173]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-19T00:00:00"}
