{"id":"https://openalex.org/W7166802488","doi":"https://doi.org/10.18653/v1/2026.acl-long.648","title":"Iterative Dual-Model Alignment for Story Evaluation","display_name":"Iterative Dual-Model Alignment for Story Evaluation","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7166802488","doi":"https://doi.org/10.18653/v1/2026.acl-long.648"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2026.acl-long.648","doi":"https://doi.org/10.18653/v1/2026.acl-long.648","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-long.648","pdf_url":"https://aclanthology.org/2026.acl-long.648.pdf","source":{"id":"https://openalex.org/S4393915740","display_name":"Proceedings of the conference - Association for Computational Linguistics. Meeting","issn_l":"0736-587X","issn":["0736-587X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://aclanthology.org/2026.acl-long.648.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139724505","display_name":"Bruce Qin","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bruce Qin","raw_affiliation_strings":["Purdue University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032121234","display_name":"Dan Goldwasser","orcid":"https://orcid.org/0000-0001-9326-8601","observed_orcids":["https://orcid.org/0000-0001-9326-8601"]},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dan Goldwasser","raw_affiliation_strings":["Purdue University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I219193219"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.4320118,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"14251","last_page":"14264"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.8223000168800354,"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":0.8223000168800354,"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":0.03629999980330467,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.014499999582767487,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/story-evaluation","display_name":"story evaluation","score":0.9179999828338623},{"id":"https://openalex.org/keywords/iterative-learning","display_name":"iterative learning","score":0.6700000166893005},{"id":"https://openalex.org/keywords/preference-optimization","display_name":"preference optimization","score":0.5479999780654907},{"id":"https://openalex.org/keywords/direct-preference-optimization","display_name":"direct preference optimization","score":0.6349999904632568},{"id":"https://openalex.org/keywords/contrastive-learning","display_name":"contrastive learning","score":0.6470000147819519},{"id":"https://openalex.org/keywords/kullback-leibler-divergence","display_name":"Kullback-Leibler divergence","score":0.4560000002384186},{"id":"https://openalex.org/keywords/textual-explanations","display_name":"textual explanations","score":0.45500001311302185},{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.5540000200271606},{"id":"https://openalex.org/keywords/model-co-evolution","display_name":"model co-evolution","score":0.46799999475479126}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5565999746322632},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4235000014305115},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3303000032901764},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.30570000410079956},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2994000017642975},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C143587482","wikidata":"https://www.wikidata.org/wiki/Q1543216","display_name":"Iterative and incremental development","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.24979999661445618},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.24959999322891235}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.acl-long.648","doi":"https://doi.org/10.18653/v1/2026.acl-long.648","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-long.648","pdf_url":"https://aclanthology.org/2026.acl-long.648.pdf","source":{"id":"https://openalex.org/S4393915740","display_name":"Proceedings of the conference - Association for Computational Linguistics. Meeting","issn_l":"0736-587X","issn":["0736-587X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.acl-long.648","doi":"https://doi.org/10.18653/v1/2026.acl-long.648","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-long.648","pdf_url":"https://aclanthology.org/2026.acl-long.648.pdf","source":{"id":"https://openalex.org/S4393915740","display_name":"Proceedings of the conference - Association for Computational Linguistics. Meeting","issn_l":"0736-587X","issn":["0736-587X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G3214379468","display_name":"RI: CAREER : Understanding Opinions by Reasoning over Socially Grounded Language","funder_award_id":"2048001","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6671297155","display_name":null,"funder_award_id":"CAREER","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166802488.pdf","grobid_xml":"https://content.openalex.org/works/W7166802488.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4402640107","https://openalex.org/W4324291707","https://openalex.org/W7172446015","https://openalex.org/W7160536847","https://openalex.org/W6931162769","https://openalex.org/W4415193085","https://openalex.org/W4402671634","https://openalex.org/W7212052473","https://openalex.org/W4417156099","https://openalex.org/W7010917313"],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2,63],"(LLMs)":[3],"can":[4],"both":[5,138],"evaluate":[6],"and":[7,19,51,104,115,140],"explain":[8],"text":[9],"quality;":[10],"however,":[11],"most":[12],"existing":[13],"evaluators":[14],"operate":[15],"as":[16],"static":[17],"classifiers":[18],"lack":[20],"the":[21,129],"ability":[22],"to":[23,75,88],"refine":[24],"their":[25],"reasoning":[26],"through":[27],"interaction.We":[28],"propose":[29],"an":[30,42],"Iterative":[31],"Alpha-Beta":[32],"Learning":[33],"framework":[34],"that":[35,46,56,128],"jointly":[36],"trains":[37],"two":[38,62],"complementary":[39],"8B":[40],"models:":[41],"Alpha":[43],"(\u03b1)":[44],"classifier":[45],"assesses":[47],"pairwise":[48],"story":[49],"engagement,":[50],"a":[52,66,92],"Beta":[53],"(\u03b2)":[54],"generator":[55],"produces":[57],"structured,":[58],"rubric-guided":[59],"comparative":[60],"explanations.The":[61],"co-evolve":[64],"within":[65],"closed":[67],"feedback":[68],"loop:":[69],"\u03b1":[70,90,101],"provides":[71],"probabilistic":[72],"preference":[73],"signals":[74],"guide":[76],"\u03b2's":[77,83,107],"Direct":[78],"Preference":[79],"Optimization":[80],"(DPO),":[81],"while":[82,110],"improved":[84],"explanations":[85],"are":[86],"reintegrated":[87],"retrain":[89],"via":[91],"KL-based":[93],"contrastive":[94],"objective.This":[95],"dual":[96],"optimization":[97],"enables":[98],"mutual":[99],"learning:":[100],"gains":[102],"interpretability":[103],"robustness":[105],"from":[106,118],"textual":[108],"rationales,":[109],"\u03b2":[111],"acquires":[112],"stronger":[113],"alignment":[114],"discriminative":[116],"precision":[117],"\u03b1's":[119],"confidence":[120],"deltas.Experiments":[121],"on":[122],"human-annotated":[123],"storypair":[124],"datasets":[125],"(HANNA)":[126],"show":[127],"proposed":[130],"system":[131],"consistently":[132],"outperforms":[133],"strong":[134],"singlemodel":[135],"baselines":[136],"in":[137],"accuracy":[139],"explanation":[141],"quality":[142],"across":[143],"multiple":[144],"iterative":[145],"rounds":[146],"1":[147],".":[148]},"counts_by_year":[],"updated_date":"2026-10-29T09:56:32.521200","created_date":"2026-07-02T00:00:00"}
