{"id":"https://openalex.org/W4416034224","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.1133","title":"Distinguishing fair from unfair compositional generalization tasks","display_name":"Distinguishing fair from unfair compositional generalization tasks","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416034224","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.1133"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.1133","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.1133","pdf_url":"https://aclanthology.org/2025.findings-emnlp.1133.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: EMNLP 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-emnlp.1133.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Ahmad Jabbar","orcid":null},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ahmad Jabbar","raw_affiliation_strings":["Stanford University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113453852","display_name":"Cleo Condoravdi","orcid":null},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cleo Condoravdi","raw_affiliation_strings":["Stanford University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042601761","display_name":"Christopher Potts","orcid":"https://orcid.org/0000-0002-7978-6055"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christopher Potts","raw_affiliation_strings":["Stanford University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"20796","last_page":"20807"},"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":0.13099999725818634,"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"}},"topics":[{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.13099999725818634,"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/T11596","display_name":"Constraint Satisfaction and Optimization","score":0.1256999969482422,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.07079999893903732,"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/generalization","display_name":"Generalization","score":0.6017000079154968},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.29100000858306885},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.25060001015663147},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.2460000067949295}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6017000079154968},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5182999968528748},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4350999891757965},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3353999853134155},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.29100000858306885},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.25060001015663147},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2460000067949295},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.24369999766349792},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.24050000309944153},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.22920000553131104}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/v1/2025.findings-emnlp.1133","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.1133","pdf_url":"https://aclanthology.org/2025.findings-emnlp.1133.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: EMNLP 2025","raw_type":"proceedings-article"},{"id":"pmh:oai:philarchive.org/rec/JABDFF","is_oa":false,"landing_page_url":"https://philarchive.org/rec/JABDFF","pdf_url":null,"source":{"id":"https://openalex.org/S4306402130","display_name":"PhilPapers (PhilPapers Foundation)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:philpapers.org/rec/JABDFF","is_oa":false,"landing_page_url":"https://philpapers.org/rec/JABDFF","pdf_url":null,"source":{"id":"https://openalex.org/S4306402131","display_name":"PhilPapers (PhilPapers Foundation)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.1133","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.1133","pdf_url":"https://aclanthology.org/2025.findings-emnlp.1133.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: EMNLP 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320315389","display_name":"Open Philanthropy Project","ror":"https://ror.org/004d1k391"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416034224.pdf","grobid_xml":"https://content.openalex.org/works/W4416034224.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Compositional":[0],"generalization":[1,102,154],"benchmarks":[2,155],"seek":[3],"to":[4,35,150],"assess":[5],"whether":[6],"learning":[7,116],"agents":[8],"can":[9,132],"successfully":[10],"combine":[11],"familiar":[12],"concepts":[13],"in":[14,27],"novel":[15],"ways.COGS":[16],"(Kim":[17],"and":[18,76,88,156],"Linzen,":[19],"2020)":[20],"provides":[21],"a":[22,77,99,115],"suite":[23],"of":[24,30,61,141,152,164],"such":[25],"tasks":[26],"the":[28,94,107,111,125,134,139,162],"area":[29],"interpretive":[31],"semantics":[32],"(mapping":[33],"sentences":[34],"logical":[36],"forms).A":[37],"noteworthy":[38],"finding":[39],"for":[40],"COGS":[41,67],"is":[42,104,119],"that":[43,54,93,103,118,130],"model":[44],"performance":[45,56],"varies":[46],"widely":[47],"across":[48],"tasks.In":[49],"this":[50],"paper,":[51],"we":[52,91],"argue":[53,92,129],"these":[55,62],"differences":[57],"reflect":[58],"deep":[59],"properties":[60],"tasks.We":[63],"focus":[64],"on":[65],"two":[66],"tasks:":[68],"an":[69],"easy":[70,95],"task":[71,79,96,113,136],"(models":[72],"are":[73],"generally":[74],"successful)":[75],"hard":[78,112,135],"(no":[80],"present-day":[81],"models":[82],"get":[83],"any":[84],"traction).Using":[85],"both":[86],"experiments":[87],"conceptual":[89],"analysis,":[90],"requires":[97],"only":[98],"single":[100],"distributional":[101],"wellsupported":[105],"by":[106,124],"training":[108,126],"data,":[109],"whereas":[110],"involves":[114],"target":[117],"ambiguous":[120],"or":[121],"even":[122],"contradicted":[123],"data.We":[127],"additionally":[128],"pretraining":[131],"disambiguate":[133],"without":[137],"compromising":[138],"goal":[140],"testing":[142],"compositional":[143,153],"generalization.Overall,":[144],"our":[145],"findings":[146],"offer":[147],"practical":[148],"guidance":[149],"designers":[151],"also":[157],"yield":[158],"new":[159],"insights":[160],"into":[161],"nature":[163],"compositionality":[165],"itself.":[166]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-08T00:00:00"}