{"id":"https://openalex.org/W4412886835","doi":"https://doi.org/10.18653/v1/2025.acl-long.53","title":"MPVStance: Mitigating Hallucinations in Stance Detection with Multi-Perspective Verification","display_name":"MPVStance: Mitigating Hallucinations in Stance Detection with Multi-Perspective Verification","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412886835","doi":"https://doi.org/10.18653/v1/2025.acl-long.53"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2025.acl-long.53","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.53","pdf_url":"https://aclanthology.org/2025.acl-long.53.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":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.acl-long.53.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"ZhaoDan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"ZhaoDan Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114237685","display_name":"Zhao Zhang","orcid":"https://orcid.org/0000-0002-8387-6133"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100405896","display_name":"Jin Zhang","orcid":"https://orcid.org/0000-0001-5416-0964"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100409297","display_name":"Hui Xu","orcid":"https://orcid.org/0000-0003-4188-022X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hui Xu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5036544715","display_name":"Xueqi Cheng","orcid":"https://orcid.org/0009-0009-3990-4414"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xueqi Cheng","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":true,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1053","last_page":"1067"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9915000200271606,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9915000200271606,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.96670001745224,"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.9049999713897705,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.7531924843788147},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5822697877883911},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2878677248954773}],"concepts":[{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.7531924843788147},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5822697877883911},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2878677248954773}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.acl-long.53","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.53","pdf_url":"https://aclanthology.org/2025.acl-long.53.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":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.acl-long.53","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.53","pdf_url":"https://aclanthology.org/2025.acl-long.53.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":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412886835.pdf","grobid_xml":"https://content.openalex.org/works/W4412886835.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/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2018871932","https://openalex.org/W2001405890"],"abstract_inverted_index":{"Stance":[0],"detection":[1,59],"is":[2],"a":[3,39,51],"pivotal":[4],"task":[5],"in":[6,18,111,115],"Natural":[7],"Language":[8,21],"Processing":[9],"(NLP),":[10],"identifying":[11],"textual":[12],"attitudes":[13],"toward":[14],"various":[15],"targets.Despite":[16],"advances":[17],"using":[19],"Large":[20],"Models":[22],"(LLMs),":[23],"challenges":[24],"persist":[25],"due":[26],"to":[27],"hallucinationmodels":[28],"generating":[29],"plausible":[30],"yet":[31],"inaccurate":[32],"content.Addressing":[33],"these":[34],"challenges,":[35],"we":[36],"introduce":[37],"MPVStance,":[38],"framework":[40],"that":[41,84,93],"incorporates":[42],"Multi-Perspective":[43],"Verification":[44],"(MPV)":[45],"with":[46],"Retrieval-Augmented":[47],"Generation":[48],"(RAG)":[49],"across":[50],"structured":[52],"five-step":[53],"verification":[54],"process.Our":[55],"method":[56],"enhances":[57],"stance":[58,112],"by":[60],"rigorously":[61],"validating":[62],"each":[63],"response":[64],"from":[65],"factual":[66],"accuracy,":[67],"logical":[68],"consistency,":[69],"contextual":[70],"relevance,":[71],"and":[72,79,88,103,109,118],"other":[73],"perspectives.Extensive":[74],"testing":[75],"on":[76],"the":[77],"SemEval-2016":[78],"VAST":[80],"datasets,":[81],"including":[82],"scenarios":[83],"challenge":[85],"existing":[86],"methods":[87],"comprehensive":[89],"ablation":[90],"studies,":[91],"demonstrates":[92],"MPVStance":[94],"significantly":[95],"outperforms":[96],"current":[97],"models.It":[98],"effectively":[99],"mitigates":[100],"hallucination":[101],"issues":[102],"sets":[104],"new":[105],"benchmarks":[106],"for":[107],"reliability":[108],"accuracy":[110],"detection,":[113],"particularly":[114],"zero-shot,":[116],"few-shot,":[117],"challenging":[119],"scenarios.":[120]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}