{"id":"https://openalex.org/W4412888364","doi":"https://doi.org/10.18653/v1/2025.findings-acl.562","title":"A Rose by Any Other Name: LLM-Generated Explanations Are Good Proxies for Human Explanations to Collect Label Distributions on NLI","display_name":"A Rose by Any Other Name: LLM-Generated Explanations Are Good Proxies for Human Explanations to Collect Label Distributions on NLI","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412888364","doi":"https://doi.org/10.18653/v1/2025.findings-acl.562"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2025.findings-acl.562","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-acl.562","pdf_url":"https://aclanthology.org/2025.findings-acl.562.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":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.findings-acl.562.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073611983","display_name":"Beiduo Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Beiduo Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008224824","display_name":"Siyao Peng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Siyao Peng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081393566","display_name":"Anna Korhonen","orcid":"https://orcid.org/0000-0002-3692-3144"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anna Korhonen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5088832285","display_name":"Barbara Plank","orcid":"https://orcid.org/0000-0002-4394-1965"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Barbara Plank","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":1.452,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.81186571,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"10777","last_page":"10802"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14351","display_name":"Statistical and Computational Modeling","score":0.9466000199317932,"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/T14351","display_name":"Statistical and Computational Modeling","score":0.9466000199317932,"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":0.9118000268936157,"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":0.9097999930381775,"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/rose","display_name":"Rose (mathematics)","score":0.7622112035751343},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5681554079055786},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.36210525035858154},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.328058123588562},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25289419293403625}],"concepts":[{"id":"https://openalex.org/C2778335665","wikidata":"https://www.wikidata.org/wiki/Q1155211","display_name":"Rose (mathematics)","level":2,"score":0.7622112035751343},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5681554079055786},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.36210525035858154},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.328058123588562},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25289419293403625},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.findings-acl.562","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-acl.562","pdf_url":"https://aclanthology.org/2025.findings-acl.562.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.562","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-acl.562","pdf_url":"https://aclanthology.org/2025.findings-acl.562.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":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320314707","display_name":"Government of the United Kingdom","ror":"https://ror.org/05wnh3t63"},{"id":"https://openalex.org/F4320314731","display_name":"UK Research and Innovation","ror":"https://ror.org/001aqnf71"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412888364.pdf","grobid_xml":"https://content.openalex.org/works/W4412888364.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/W2314041892","https://openalex.org/W1820089824","https://openalex.org/W4252203162","https://openalex.org/W4255063205","https://openalex.org/W2960895741","https://openalex.org/W3000283345","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Disagreement":[0],"in":[1,10,61],"human":[2,11,25,80,95,102,110,135,143],"labeling":[3],"is":[4,48],"ubiquitous,":[5],"and":[6,29,86,106,109,153],"can":[7,34,55],"be":[8,56],"captured":[9],"judgment":[12,96],"distributions":[13],"(HJDs).Recent":[14],"research":[15],"has":[16],"shown":[17],"that":[18,115],"explanations":[19,44,63,75,117,126,144],"provide":[20],"valuable":[21],"information":[22],"for":[23,45,64,76,120],"understanding":[24],"label":[26,47],"variation":[27],"(HLV)":[28],"large":[30],"language":[31],"models":[32],"(LLMs)":[33],"approximate":[35,94],"HJD":[36],"from":[37,140],"a":[38,77],"few":[39,78],"human-provided":[40],"labelexplanation":[41],"pairs.However,":[42],"collecting":[43],"every":[46],"still":[49],"time-consuming.This":[50],"paper":[51],"examines":[52],"whether":[53],"LLMs":[54,69],"used":[57],"to":[58,72,84,93,122,130,145],"replace":[59],"humans":[60],"generating":[62],"approximating":[65],"HJD.Specifically,":[66],"we":[67],"use":[68],"as":[70],"annotators":[71],"generate":[73],"model":[74],"given":[79],"labels.We":[81],"test":[82,107,157],"ways":[83],"obtain":[85],"combine":[87],"these":[88],"labelexplanations":[89],"with":[90,103,134,142],"the":[91,100],"goal":[92],"distributions.We":[97],"further":[98],"compare":[99],"resulting":[101],"model-generated":[104],"explanations,":[105],"automatic":[108],"explanation":[111],"selection.Our":[112],"experiments":[113],"show":[114],"LLM":[116],"are":[118,150],"promising":[119],"NLI:":[121],"estimate":[123],"HJDs,":[124],"generated":[125],"yield":[127],"comparable":[128],"results":[129,138],"human's":[131],"when":[132],"provided":[133],"labels.Importantly,":[136],"our":[137],"generalize":[139],"datasets":[141,147],"i)":[146],"where":[148],"they":[149],"not":[151],"available":[152],"ii)":[154],"challenging":[155],"out-of-distribution":[156],"sets.":[158]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-08-18T07:49:30.821534","created_date":"2025-10-10T00:00:00"}