{"id":"https://openalex.org/W4412888248","doi":"https://doi.org/10.18653/v1/2025.findings-acl.723","title":"CausalAbstain: Enhancing Multilingual LLMs with Causal Reasoning for Trustworthy Abstention","display_name":"CausalAbstain: Enhancing Multilingual LLMs with Causal Reasoning for Trustworthy Abstention","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412888248","doi":"https://doi.org/10.18653/v1/2025.findings-acl.723"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2025.findings-acl.723","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-acl.723","pdf_url":"https://aclanthology.org/2025.findings-acl.723.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2025","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.findings-acl.723.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033224121","display_name":"Yuxi Sun","orcid":"https://orcid.org/0000-0002-3040-5880"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuxi Sun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119181382","display_name":"Aoqi Zuo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aoqi Zuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016965594","display_name":"Wei Gao","orcid":"https://orcid.org/0000-0001-7814-3712"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Gao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5025387535","display_name":"Jing Ma","orcid":"https://orcid.org/0000-0003-3365-656X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jing Ma","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.5175,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.93059589,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"14060","last_page":"14076"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9793999791145325,"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/T10028","display_name":"Topic Modeling","score":0.9793999791145325,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9760000109672546,"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/trustworthiness","display_name":"Trustworthiness","score":0.8395489454269409},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6311321258544922},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.37333551049232483},{"id":"https://openalex.org/keywords/cognitive-science","display_name":"Cognitive science","score":0.3276721239089966},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.23889145255088806}],"concepts":[{"id":"https://openalex.org/C153701036","wikidata":"https://www.wikidata.org/wiki/Q659974","display_name":"Trustworthiness","level":2,"score":0.8395489454269409},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6311321258544922},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.37333551049232483},{"id":"https://openalex.org/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","level":1,"score":0.3276721239089966},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.23889145255088806}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.findings-acl.723","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-acl.723","pdf_url":"https://aclanthology.org/2025.findings-acl.723.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2025","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.findings-acl.723","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-acl.723","pdf_url":"https://aclanthology.org/2025.findings-acl.723.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.41999998688697815,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320316083","display_name":"Tencent","ror":"https://ror.org/00hhjss72"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412888248.pdf","grobid_xml":"https://content.openalex.org/works/W4412888248.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2076536433","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W90316445","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109"],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"often":[4],"exhibit":[5],"knowledge":[6,16,124],"disparities":[7],"across":[8],"languages.Encouraging":[9],"LLMs":[10,42,73],"to":[11,22,76,84],"abstain":[12],"when":[13],"faced":[14],"with":[15,102],"gaps":[17],"is":[18],"a":[19,63,69],"promising":[20],"strategy":[21],"reduce":[23],"hallucinations":[24],"in":[25,38,56,104],"multilingual":[26,31,110],"settings.Current":[27],"abstention":[28,100],"strategies":[29],"for":[30],"scenarios":[32],"primarily":[33],"rely":[34],"on":[35,116],"generating":[36],"feedback":[37,80,97],"various":[39],"languages":[40],"using":[41],"and":[43,54,82,98,109,122,128],"performing":[44],"self-reflection.However,":[45],"these":[46],"methods":[47],"can":[48],"be":[49],"adversely":[50],"impacted":[51],"by":[52],"inaccuracies":[53],"biases":[55],"the":[57,86],"generated":[58,79],"feedback.To":[59],"address":[60],"this,":[61],"from":[62],"causal":[64],"perspective,":[65],"we":[66],"introduce":[67],"Causal-Abstain,":[68],"method":[70],"that":[71,92],"helps":[72],"determine":[74],"whether":[75],"utilize":[77],"multiple":[78],"responses":[81],"how":[83],"identify":[85],"most":[87],"useful":[88],"ones.Extensive":[89],"experiments":[90],"demonstrate":[91],"CausalAbstain":[93],"effectively":[94],"selects":[95],"helpful":[96],"enhances":[99],"decisions":[101],"interpretability":[103],"both":[105],"native":[106],"language":[107],"(CASUAL-NATIVE)":[108],"(CAUSAL-MULTI)":[111],"settings,":[112],"outperforming":[113],"strong":[114],"baselines":[115],"two":[117],"benchmark":[118],"datasets":[119],"covering":[120],"encyclopedic":[121],"commonsense":[123],"QA":[125],"tasks.Our":[126],"code":[127],"data":[129],"are":[130],"open-sourced":[131],"at":[132],"https:":[133],"//github.com/peachch/CausalAbstain.":[134]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
