{"id":"https://openalex.org/W4403791661","doi":"https://doi.org/10.1145/3664647.3681263","title":"Causal-driven Large Language Models with Faithful Reasoning for Knowledge Question Answering","display_name":"Causal-driven Large Language Models with Faithful Reasoning for Knowledge Question Answering","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4403791661","doi":"https://doi.org/10.1145/3664647.3681263"},"language":"en","primary_location":{"id":"doi:10.1145/3664647.3681263","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3681263","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5058918328","display_name":"Jiawei Wang","orcid":"https://orcid.org/0000-0002-6601-2958"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawei Wang","raw_affiliation_strings":["Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-6601-2958","affiliations":[{"raw_affiliation_string":"Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078164599","display_name":"Da Cao","orcid":"https://orcid.org/0000-0002-2611-2559"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Da Cao","raw_affiliation_strings":["Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-2611-2559","affiliations":[{"raw_affiliation_string":"Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017893281","display_name":"Shaofei Lu","orcid":"https://orcid.org/0000-0003-2183-4314"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaofei Lu","raw_affiliation_strings":["Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0003-2183-4314","affiliations":[{"raw_affiliation_string":"Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083597502","display_name":"Zhanchang Ma","orcid":"https://orcid.org/0009-0006-7705-3639"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhanchang Ma","raw_affiliation_strings":["Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0009-0006-7705-3639","affiliations":[{"raw_affiliation_string":"Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024669782","display_name":"Junbin Xiao","orcid":"https://orcid.org/0000-0001-5573-6195"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Junbin Xiao","raw_affiliation_strings":["National University of Singapore, Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0001-5573-6195","affiliations":[{"raw_affiliation_string":"National University of Singapore, Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089404640","display_name":"Tat\u2010Seng Chua","orcid":"https://orcid.org/0000-0001-6097-7807"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Tat-Seng Chua","raw_affiliation_strings":["National University of Singapore, Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0001-6097-7807","affiliations":[{"raw_affiliation_string":"National University of Singapore, Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0936,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.79223911,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"4331","last_page":"4340"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9987999796867371,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9976999759674072,"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/question-answering","display_name":"Question answering","score":0.825420618057251},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7334434390068054},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5738640427589417},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4845365881919861},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4190676510334015}],"concepts":[{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.825420618057251},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7334434390068054},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5738640427589417},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4845365881919861},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4190676510334015}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3664647.3681263","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3681263","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.550000011920929,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W178384805","https://openalex.org/W2593831809","https://openalex.org/W2745461083","https://openalex.org/W2964067226","https://openalex.org/W2966683369","https://openalex.org/W2981919786","https://openalex.org/W3034917890","https://openalex.org/W3171353004","https://openalex.org/W3204510023","https://openalex.org/W4290864872","https://openalex.org/W4292779060","https://openalex.org/W4296605665","https://openalex.org/W4367046983","https://openalex.org/W4377130677","https://openalex.org/W4385569968","https://openalex.org/W4387968135","https://openalex.org/W4387968292","https://openalex.org/W4387968461","https://openalex.org/W4388185892","https://openalex.org/W4388189274","https://openalex.org/W4389518686","https://openalex.org/W4389518784","https://openalex.org/W4389520310","https://openalex.org/W4393160204","https://openalex.org/W4396758715","https://openalex.org/W4400531852","https://openalex.org/W6778883912"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2384605597","https://openalex.org/W2387743295","https://openalex.org/W4288267738","https://openalex.org/W3204019825","https://openalex.org/W2964413124","https://openalex.org/W4388937922","https://openalex.org/W3113264705"],"abstract_inverted_index":{"In":[0],"Large":[1],"Language":[2],"Models":[3],"(LLMs),":[4],"text":[5],"generation":[6],"that":[7,65,107],"involves":[8,77],"knowledge":[9,66,114,144],"representation":[10],"is":[11,67,159],"often":[12,29],"fraught":[13],"with":[14],"the":[15,35,40,47,82,85,88,95,109,112,118,121,177,197,202],"risk":[16],"of":[17,42,61,87,97,120,161,201],"\"hallucinations'',":[18],"where":[19],"models":[20],"confidently":[21],"produce":[22],"erroneous":[23],"or":[24,38],"fabricated":[25],"content.":[26],"These":[27],"inaccuracies":[28],"stem":[30],"from":[31,39,57,124],"intrinsic":[32],"biases":[33,45],"in":[34,147],"pre-training":[36],"stage":[37],"incorporation":[41],"human":[43],"preference":[44],"during":[46],"fine-tuning":[48],"process.":[49],"To":[50],"mitigate":[51],"these":[52],"issues,":[53],"we":[54,128],"take":[55],"inspiration":[56],"Goldman's":[58],"causal":[59,79,105,126,137,148,167],"theory":[60,93],"knowledge,":[62],"which":[63,195],"asserts":[64],"not":[68],"merely":[69],"about":[70],"having":[71],"a":[72,78,104,141],"true":[73],"belief":[74,83,169],"but":[75],"also":[76],"connection":[80],"between":[81,111],"and":[84,115,171,184,192,199],"truth":[86],"proposition.":[89],"We":[90],"instantiate":[91],"this":[92,136],"within":[94],"context":[96],"Knowledge":[98],"Question":[99],"Answering":[100],"(KQA)":[101],"by":[102,180],"constructing":[103],"graph":[106],"delineates":[108],"pathways":[110],"candidate":[113],"belief.":[116],"Through":[117],"application":[119],"do-calculus":[122],"rules":[123],"structural":[125],"models,":[127],"devise":[129],"an":[130],"unbiased":[131],"estimation":[132],"framework":[133,153],"based":[134],"on":[135,190],"graph,":[138],"thereby":[139],"establishing":[140],"methodology":[142],"for":[143,155],"modeling":[145],"grounded":[146],"inference.":[149],"The":[150],"resulting":[151],"CORE":[152,203],"(short":[154],"\"Causal":[156],"knOwledge":[157],"REasoning'')":[158],"comprised":[160],"four":[162],"essential":[163],"components:":[164],"question":[165],"answering,":[166],"reasoning,":[168],"scoring,":[170],"refinement.":[172],"Together,":[173],"they":[174],"synergistically":[175],"improve":[176],"KQA":[178],"system":[179],"fostering":[181],"faithful":[182],"reasoning":[183],"introspection.":[185],"Extensive":[186],"experiments":[187],"are":[188],"conducted":[189],"ScienceQA":[191],"HotpotQA":[193],"datasets,":[194],"demonstrate":[196],"effectiveness":[198],"rationality":[200],"framework.":[204]},"counts_by_year":[{"year":2025,"cited_by_count":5}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}