{"id":"https://openalex.org/W4416037380","doi":"https://doi.org/10.18653/v1/2025.emnlp-industry.65","title":"Transparent Reference-free Automated Evaluation of Open-Ended User Survey Responses","display_name":"Transparent Reference-free Automated Evaluation of Open-Ended User Survey Responses","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416037380","doi":"https://doi.org/10.18653/v1/2025.emnlp-industry.65"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2025.emnlp-industry.65","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.emnlp-industry.65","pdf_url":"https://aclanthology.org/2025.emnlp-industry.65.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 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.emnlp-industry.65.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041367428","display_name":"Subin An","orcid":"https://orcid.org/0000-0002-4386-6934"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Subin An","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090163777","display_name":"Yonghoon Ji","orcid":"https://orcid.org/0000-0002-0828-6627"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yugyeong Ji","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100383376","display_name":"Junyoung Kim","orcid":"https://orcid.org/0000-0002-3574-6908"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Junyoung Kim","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082412286","display_name":"Heejin Kook","orcid":"https://orcid.org/0009-0009-1240-1315"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heejin Kook","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088683554","display_name":"Yang L\u00fc","orcid":"https://orcid.org/0000-0002-0583-2688"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang Lu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5004908904","display_name":"Josh Seltzer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Josh Seltzer","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":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.37686406,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"963","last_page":"982"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11539","display_name":"Survey Methodology and Nonresponse","score":0.5184000134468079,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11539","display_name":"Survey Methodology and Nonresponse","score":0.5184000134468079,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13030","display_name":"Survey Sampling and Estimation Techniques","score":0.18140000104904175,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10467","display_name":"Psychometric Methodologies and Testing","score":0.03929999843239784,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/automation","display_name":"Automation","score":0.28349998593330383},{"id":"https://openalex.org/keywords/data-collection","display_name":"Data collection","score":0.27880001068115234},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.2786000072956085},{"id":"https://openalex.org/keywords/user-interface","display_name":"User interface","score":0.2736999988555908},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.2475000023841858}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6044999957084656},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2980000078678131},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27959999442100525},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C89505385","wikidata":"https://www.wikidata.org/wiki/Q47146","display_name":"User interface","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.2475000023841858},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.24220000207424164},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.23989999294281006}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.emnlp-industry.65","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.emnlp-industry.65","pdf_url":"https://aclanthology.org/2025.emnlp-industry.65.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 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.emnlp-industry.65","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.emnlp-industry.65","pdf_url":"https://aclanthology.org/2025.emnlp-industry.65.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 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1248055536","display_name":null,"funder_award_id":"RS-2022-00143911","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"},{"id":"https://openalex.org/G4700831490","display_name":null,"funder_award_id":"2022-","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"}],"funders":[{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416037380.pdf","grobid_xml":"https://content.openalex.org/works/W4416037380.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Open-ended":[0],"survey":[1,60,82],"responses":[2,11,42],"provide":[3],"valuable":[4],"insights":[5],"in":[6,77],"marketing":[7],"research,":[8],"but":[9,19,98],"low-quality":[10],"not":[12,93],"only":[13,94],"burden":[14],"researchers":[15],"with":[16,43,118],"manual":[17],"filtering":[18,63],"also":[20,99],"risk":[21],"leading":[22],"to":[23],"misleading":[24],"conclusions,":[25],"underscoring":[26],"the":[27],"need":[28],"for":[29,58,104],"effective":[30],"evaluation.Existing":[31],"automatic":[32],"evaluation":[33,54],"methods":[34],"target":[35],"LLM-generated":[36],"text":[37],"and":[38,70,86,112],"inadequately":[39],"assess":[40],"human-written":[41],"their":[44],"distinct":[45],"characteristics.To":[46],"address":[47],"such":[48,107],"characteristics,":[49],"we":[50],"propose":[51],"a":[52],"two-stage":[53],"framework":[55,92],"specifically":[56],"designed":[57],"human":[59],"responses.First,":[61],"gibberish":[62],"removes":[64],"nonsensical":[65],"responses.Then,":[66],"three":[67],"dimensions-effort,":[68],"relevance,":[69],"completeness-are":[71],"evaluated":[72],"using":[73],"LLM":[74],"capabilities,":[75],"grounded":[76],"empirical":[78],"analysis":[79],"of":[80],"real-world":[81,105],"data.Validation":[83],"on":[84],"English":[85],"Korean":[87],"datasets":[88],"shows":[89],"that":[90],"our":[91],"outperforms":[95],"existing":[96],"metrics":[97],"demonstrates":[100],"high":[101],"practical":[102],"applicability":[103],"applications":[106],"as":[108],"response":[109,113],"quality":[110],"prediction":[111],"rejection,":[114],"showing":[115],"strong":[116],"correlations":[117],"expert":[119],"assessment.":[120]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-11-08T00:00:00"}