{"id":"https://openalex.org/W7166857227","doi":"https://doi.org/10.18653/v1/2026.acl-long.1458","title":"SAFO: Stable Adaptive Fairness Optimization for LLM-Based Social Survey Simulation","display_name":"SAFO: Stable Adaptive Fairness Optimization for LLM-Based Social Survey Simulation","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7166857227","doi":"https://doi.org/10.18653/v1/2026.acl-long.1458"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.acl-long.1458","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-long.1458","pdf_url":"https://aclanthology.org/2026.acl-long.1458.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 64th 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/2026.acl-long.1458.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077582679","display_name":"Chenxi Lin","orcid":"https://orcid.org/0009-0001-9598-6559"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chenxi Lin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139816487","display_name":"Zhuoren Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhuoren Jiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139808544","display_name":"Kaisong Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kaisong Song","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126025015","display_name":"Yiquan Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yiquan Wu","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":0.85812044,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"31626","last_page":"31654"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.10440000146627426,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.10440000146627426,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.0934000015258789,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"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/T12592","display_name":"Opinion Dynamics and Social Influence","score":0.09070000052452087,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.3359000086784363},{"id":"https://openalex.org/keywords/survey-data-collection","display_name":"Survey data collection","score":0.3278999924659729},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.27900001406669617},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.26840001344680786}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5773000121116638},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.36570000648498535},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C198477413","wikidata":"https://www.wikidata.org/wiki/Q7647069","display_name":"Survey data collection","level":2,"score":0.3278999924659729},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.2892000079154968},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.2687999904155731},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2533000111579895},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.acl-long.1458","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-long.1458","pdf_url":"https://aclanthology.org/2026.acl-long.1458.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 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.1458","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-long.1458","pdf_url":"https://aclanthology.org/2026.acl-long.1458.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 64th 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/F4320318398","display_name":"Ant Group","ror":null},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166857227.pdf","grobid_xml":"https://content.openalex.org/works/W7166857227.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Ensuring":[0],"fairness":[1,91],"in":[2],"social":[3],"survey":[4,85,136],"simulation":[5,86],"is":[6,16,179],"critical,":[7],"as":[8,18],"biased":[9],"outputs":[10],"can":[11,57],"misrepresent":[12],"underrepresented":[13],"groups.This":[14],"issue":[15],"growing":[17],"large":[19],"language":[20],"models":[21],"(LLMs)":[22],"are":[23],"increasingly":[24],"used":[25],"for":[26,71,83],"this":[27],"task.However,":[28],"standard":[29],"finetuning":[30],"based":[31],"on":[32,133],"Empirical":[33],"Risk":[34],"Minimization":[35],"(ERM)":[36],"often":[37],"under-optimizes":[38],"minority":[39,149],"groups,":[40],"causing":[41],"substantial":[42],"subgroup":[43],"disparities.Distributionally":[44],"robust":[45],"Optimization":[46],"(DRO)":[47],"methods":[48],"reduce":[49],"worst-case":[50,55],"errors,":[51],"but":[52],"their":[53],"strict":[54],"selection":[56],"lead":[58],"to":[59,159],"noisy":[60],"and":[61,74,92,110,114,129,142,151,172],"unstable":[62],"optimization":[63,81],"under":[64],"demographic":[65],"sparsity.These":[66],"issues":[67],"create":[68],"intertwined":[69],"challenges":[70],"fairness,":[72],"convergence":[73],"stability.We":[75],"propose":[76],"SAFO,":[77],"a":[78,116,165],"dynamic":[79],"utility-fairness":[80,123],"framework":[82],"LLM-based":[84],"that":[87,99,106,119,145],"explicitly":[88],"targets":[89],"both":[90],"training":[93],"stability.SAFO":[94],"combines":[95],"(i)":[96],"an":[97,104],"Optimizer":[98],"preserves":[100],"mean-loss":[101],"utility,":[102],"(ii)":[103],"Adversary":[105],"performs":[107],"temperature-controlled,":[108],"EMAsmoothed":[109],"loss-driven":[111],"group":[112],"reweighting,":[113],"(iii)":[115],"Nash-inspired":[117],"Regulator":[118],"adaptively":[120],"adjusts":[121],"the":[122,140],"trade-off":[124],"by":[125,157],"tracking":[126],"weak-group":[127],"gains":[128],"collateral":[130],"utility":[131],"damages.Experiments":[132],"three":[134],"large-scale":[135],"datasets":[137],"from":[138],"China,":[139],"U.S.,":[141],"Europe":[143],"show":[144],"SAFO":[146],"consistently":[147],"improves":[148],"performance":[150],"social-welfare":[152],"metrics.It":[153],"reduces":[154],"worst-group":[155],"gaps":[156],"up":[158],"12.7%,":[160],"maintains":[161],"overall":[162],"accuracy":[163],"with":[164],"mean":[166],"change":[167],"of":[168],"less":[169],"than":[170],"0.3%":[171],"lowers":[173],"variance":[174],"across":[175],"random":[176],"seeds.Our":[177],"code":[178],"available":[180],"at":[181],"https://github.com/PiLab-ZJU/SAFO.":[182]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-07-02T00:00:00"}
