{"id":"https://openalex.org/W7204461732","doi":"https://doi.org/10.18653/2026.emnlp-main.259","title":"GGSS: Geodesic-Gated Spherical Steering for Inference-Time Debiasing of Generative Vision-Language Models","display_name":"GGSS: Geodesic-Gated Spherical Steering for Inference-Time Debiasing of Generative Vision-Language Models","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7204461732","doi":"https://doi.org/10.18653/2026.emnlp-main.259"},"language":"en","primary_location":{"id":"doi:10.18653/2026.emnlp-main.259","doi":"https://doi.org/10.18653/2026.emnlp-main.259","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.emnlp-main.259","pdf_url":"https://aclanthology.org/2026.emnlp-main.259.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://aclanthology.org/2026.emnlp-main.259.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133879326","display_name":"Yiqun Sun","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yiqun Sun","raw_affiliation_strings":["Magellan Technology Research Institute (MTRI) ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Magellan Technology Research Institute (MTRI) ,","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148422367","display_name":"Junyu Chen","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Junyu Chen","raw_affiliation_strings":["National University of Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136124800","display_name":"Pengfei Wei","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pengfei Wei","raw_affiliation_strings":["Magellan Technology Research Institute (MTRI) ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Magellan Technology Research Institute (MTRI) ,","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023745440","display_name":"Lawrence Hsieh","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lawrence B. Hsieh","raw_affiliation_strings":["Magellan Technology Research Institute (MTRI) ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Magellan Technology Research Institute (MTRI) ,","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.7172,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.93354958,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"5875","last_page":"5894"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.7554000020027161,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.7554000020027161,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.06620000302791595,"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":0.06120000034570694,"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/demographic-bias","display_name":"demographic bias","score":0.8320000171661377},{"id":"https://openalex.org/keywords/adaptive-gating","display_name":"adaptive gating","score":0.5680000185966492},{"id":"https://openalex.org/keywords/gender-bias","display_name":"gender bias","score":0.7089999914169312},{"id":"https://openalex.org/keywords/racial-bias","display_name":"racial bias","score":0.6859999895095825}],"concepts":[{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.9144999980926514},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6323000192642212},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5838000178337097},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.5611000061035156},{"id":"https://openalex.org/C21308566","wikidata":"https://www.wikidata.org/wiki/Q7169365","display_name":"Permutation (music)","level":2,"score":0.5605999827384949},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.541100025177002},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.510200023651123},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5077000260353088},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.501800000667572},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4683000147342682},{"id":"https://openalex.org/C56951928","wikidata":"https://www.wikidata.org/wiki/Q3539213","display_name":"Trimming","level":2,"score":0.45660001039505005},{"id":"https://openalex.org/C165818556","wikidata":"https://www.wikidata.org/wiki/Q213488","display_name":"Geodesic","level":2,"score":0.44699999690055847},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.4002000093460083},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.39419999718666077},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35670000314712524},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35499998927116394},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3305000066757202},{"id":"https://openalex.org/C2776299755","wikidata":"https://www.wikidata.org/wiki/Q432449","display_name":"Carry (investment)","level":2,"score":0.319599986076355},{"id":"https://openalex.org/C57489055","wikidata":"https://www.wikidata.org/wiki/Q190046","display_name":"Ellipsoid","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C2779844322","wikidata":"https://www.wikidata.org/wiki/Q2919140","display_name":"Tilt (camera)","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2856000065803528},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C2776196576","wikidata":"https://www.wikidata.org/wiki/Q196113","display_name":"Camouflage","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C83248878","wikidata":"https://www.wikidata.org/wiki/Q344000","display_name":"Active appearance model","level":3,"score":0.26330000162124634}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/2026.emnlp-main.259","doi":"https://doi.org/10.18653/2026.emnlp-main.259","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.emnlp-main.259","pdf_url":"https://aclanthology.org/2026.emnlp-main.259.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2608.25375","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.25375","pdf_url":"https://arxiv.org/pdf/2608.25375","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2608.25375","doi":"https://doi.org/10.48550/arxiv.2608.25375","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.25375","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.18653/2026.emnlp-main.259","doi":"https://doi.org/10.18653/2026.emnlp-main.259","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.emnlp-main.259","pdf_url":"https://aclanthology.org/2026.emnlp-main.259.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.7578999996185303,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204461732.pdf","grobid_xml":"https://content.openalex.org/works/W7204461732.grobid-xml"},"referenced_works_count":16,"referenced_works":["https://openalex.org/W4319048499","https://openalex.org/W4386081218","https://openalex.org/W4387355345","https://openalex.org/W4388514540","https://openalex.org/W4393213350","https://openalex.org/W4403345778","https://openalex.org/W4403995403","https://openalex.org/W4407571343","https://openalex.org/W4415798312","https://openalex.org/W4416780398","https://openalex.org/W4416955620","https://openalex.org/W7128405610","https://openalex.org/W7133214890","https://openalex.org/W7154339225","https://openalex.org/W7160872231","https://openalex.org/W7166841014"],"related_works":["https://openalex.org/W7106715728","https://openalex.org/W7090349954","https://openalex.org/W4414360669","https://openalex.org/W4415798254","https://openalex.org/W7136305228","https://openalex.org/W4403995403","https://openalex.org/W7204197037","https://openalex.org/W4319048499","https://openalex.org/W4415797405","https://openalex.org/W6979277874"],"abstract_inverted_index":{"Generative":[0],"vision-language":[1],"models":[2,42],"(VLMs)":[3],"are":[4],"increasingly":[5],"used":[6],"in":[7,22],"human-centered":[8],"settings,":[9],"yet":[10],"they":[11],"can":[12],"produce":[13],"demographically":[14],"biased":[15],"outputs":[16],"even":[17],"when":[18],"images":[19],"differ":[20],"only":[21],"controlled":[23],"attributes":[24],"such":[25],"as":[26],"perceived":[27],"race":[28],"or":[29,40],"gender.However,":[30],"existing":[31],"inference-time":[32],"debiasers":[33],"were":[34],"largely":[35],"designed":[36],"for":[37],"static":[38],"embeddings":[39],"CLIP-like":[41],"rather":[43],"than":[44],"generative":[45,86],"VLMs.We":[46],"propose":[47],"GGSS-Geodesic-Gated":[48],"Spherical":[49],"Steering-a":[50],"norm-preserving":[51],"intervention":[52],"that":[53,79],"discovers":[54],"a":[55,98],"counterfactual":[56],"bias":[57,107,119],"subspace":[58],"on":[59,77,120,125],"the":[60,116,142],"unit":[61],"hypersphere,":[62],"steers":[63],"visual":[64],"tokens":[65,78],"along":[66],"geodesic":[67],"arcs,":[68],"and":[69,94,105],"uses":[70],"an":[71],"adaptive":[72],"gate":[73],"to":[74],"focus":[75],"correction":[76],"carry":[80],"stronger":[81],"demographic":[82],"signal.We":[83],"evaluate":[84],"four":[85,122,128],"VLMs":[87],"against":[88],"ten":[89],"adapted":[90],"inferencetime":[91],"debiasing":[92],"baselines":[93],"prompt-based":[95],"mitigation":[96],"under":[97,130],"single":[99],"operating-point":[100],"protocol":[101],"across":[102],"categorical,":[103],"pairwise,":[104],"occupationgender":[106],"tests,":[108,133],"while":[109,134],"also":[110],"measuring":[111],"general":[112],"visual-language":[113],"capability.GGSS":[114],"achieves":[115],"lowest":[117],"average":[118],"all":[121],"models,":[123],"significant":[124],"three":[126],"of":[127,141],"backbones":[129],"paired":[131],"permutation":[132],"preserving":[135],"MMStar":[136],"accuracy":[137],"within":[138],"\u00b10.6":[139],"p.p.":[140],"unsteered":[143],"baseline.Code":[144],"is":[145],"available":[146],"at":[147],"https://github.c":[148],"om/dukesun99/GGSS.":[149]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2026-08-28T00:00:00"}