{"id":"https://openalex.org/W7126396010","doi":"https://doi.org/10.18653/v1/2024.findings-eacl.50","title":"Investigating grammatical abstraction in language models using few-shot learning of novel noun gender","display_name":"Investigating grammatical abstraction in language models using few-shot learning of novel noun gender","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W7126396010","doi":"https://doi.org/10.18653/v1/2024.findings-eacl.50"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2024.findings-eacl.50","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.findings-eacl.50","pdf_url":"https://aclanthology.org/2024.findings-eacl.50.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: EACL 2024","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2024.findings-eacl.50.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031532002","display_name":"Priyanka Sukumaran","orcid":"https://orcid.org/0000-0002-1537-3841"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Priyanka Sukumaran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002438004","display_name":"Conor Houghton","orcid":"https://orcid.org/0000-0001-5017-9473"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Conor Houghton","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5077751246","display_name":"Nina Kazanina","orcid":"https://orcid.org/0000-0001-7737-4279"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nina Kazanina","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"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":0.57250029,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"747","last_page":"765"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.45989999175071716,"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":0.45989999175071716,"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/T12380","display_name":"Authorship Attribution and Profiling","score":0.13339999318122864,"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/T13629","display_name":"Text Readability and Simplification","score":0.07540000230073929,"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/abstraction","display_name":"Abstraction","score":0.6432999968528748},{"id":"https://openalex.org/keywords/noun","display_name":"Noun","score":0.4936000108718872},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.3864000141620636},{"id":"https://openalex.org/keywords/noun-phrase","display_name":"Noun phrase","score":0.37790000438690186},{"id":"https://openalex.org/keywords/language-acquisition","display_name":"Language acquisition","score":0.32899999618530273},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.3224000036716461}],"concepts":[{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.6432999968528748},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6266999840736389},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5720000267028809},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.5656999945640564},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5390999913215637},{"id":"https://openalex.org/C121934690","wikidata":"https://www.wikidata.org/wiki/Q1084","display_name":"Noun","level":2,"score":0.4936000108718872},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3864000141620636},{"id":"https://openalex.org/C153962237","wikidata":"https://www.wikidata.org/wiki/Q1401131","display_name":"Noun phrase","level":3,"score":0.37790000438690186},{"id":"https://openalex.org/C74672266","wikidata":"https://www.wikidata.org/wiki/Q815859","display_name":"Language acquisition","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3224000036716461},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3122999966144562},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.29760000109672546},{"id":"https://openalex.org/C26022165","wikidata":"https://www.wikidata.org/wiki/Q8091","display_name":"Grammar","level":2,"score":0.29120001196861267},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C129792486","wikidata":"https://www.wikidata.org/wiki/Q1050419","display_name":"Language identification","level":3,"score":0.25290000438690186}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2024.findings-eacl.50","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.findings-eacl.50","pdf_url":"https://aclanthology.org/2024.findings-eacl.50.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: EACL 2024","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2024.findings-eacl.50","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.findings-eacl.50","pdf_url":"https://aclanthology.org/2024.findings-eacl.50.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: EACL 2024","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.4451354742050171,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"},{"score":0.423585444688797,"id":"https://metadata.un.org/sdg/5","display_name":"Gender equality"}],"awards":[{"id":"https://openalex.org/G2407871388","display_name":null,"funder_award_id":"RF-2021-533","funder_id":"https://openalex.org/F4320319993","funder_display_name":"Leverhulme Trust"},{"id":"https://openalex.org/G8859836646","display_name":null,"funder_award_id":"108899/B/15/Z","funder_id":"https://openalex.org/F4320311904","funder_display_name":"Wellcome Trust"},{"id":"https://openalex.org/G954940907","display_name":null,"funder_award_id":"RF-2021","funder_id":"https://openalex.org/F4320319993","funder_display_name":"Leverhulme Trust"}],"funders":[{"id":"https://openalex.org/F4320311904","display_name":"Wellcome Trust","ror":"https://ror.org/029chgv08"},{"id":"https://openalex.org/F4320319993","display_name":"Leverhulme Trust","ror":"https://ror.org/012mzw131"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7126396010.pdf","grobid_xml":"https://content.openalex.org/works/W7126396010.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Humans":[0],"can":[1,29,56],"learn":[2],"a":[3,43,53,73,78,119,178,205],"new":[4],"word":[5],"and":[6,25,36,52,86,109,209],"infer":[7],"its":[8],"grammatical":[9,23,61,83,156],"properties":[10,21],"from":[11,39,77,103],"very":[12],"few":[13,79],"examples.They":[14],"have":[15],"an":[16,50,159,186],"abstract":[17,160],"notion":[18],"of":[19,60,72,150,172],"linguistic":[20],"like":[22,162,200],"gender":[24,62,71,102,113,124,141,157,191,207],"agreement":[26,84,88,115],"rules":[27],"that":[28,94,137,153,196],"be":[30],"applied":[31,131],"to":[32,47,105,132,168],"novel":[33,74,100,189],"syntactic":[34],"contexts":[35],"words.Drawing":[37],"inspiration":[38],"psycholinguistics,":[40],"we":[41,184],"conduct":[42],"noun":[44,75,101,190],"learning":[45,69,107,192],"experiment":[46],"assess":[48],"whether":[49],"LSTM":[51],"decoder-only":[54],"transformer":[55],"achieve":[57],"human-like":[58],"abstraction":[59],"in":[63,81,89],"French.Language":[64],"models":[65,97,138,151],"were":[66,129],"tasked":[67],"with":[68,118,181],"the":[70,111,122,126,133,144,147,170],"embedding":[76,134],"examples":[80,108],"one":[82,104],"context":[85],"predicting":[87],"another,":[90],"unseen":[91],"context.We":[92],"find":[93],"both":[95],"language":[96,201],"effectively":[98],"generalise":[99],"two":[106],"apply":[110],"learnt":[112],"across":[114],"contexts,":[116],"albeit":[117],"bias":[120,208],"for":[121],"masculine":[123,206],"category.Importantly,":[125],"fewshot":[127],"updates":[128],"only":[130],"layers,":[135],"demonstrating":[136],"encode":[139],"sufficient":[140],"information":[142],"within":[143],"wordembedding":[145],"space.While":[146],"generalisation":[148],"behaviour":[149],"suggests":[152],"they":[154],"represent":[155],"as":[158],"category,":[161],"humans,":[163],"further":[164],"work":[165],"is":[166,176],"needed":[167],"explore":[169],"details":[171],"how":[173],"exactly":[174],"this":[175],"implemented.For":[177],"comparative":[179],"perspective":[180],"human":[182],"behaviour,":[183],"conducted":[185],"analogous":[187],"one-shot":[188,213],"experiment,":[193],"which":[194],"revealed":[195],"native":[197],"French":[198],"speakers,":[199],"models,":[202],"also":[203],"exhibited":[204],"are":[210],"not":[211],"excellent":[212],"learners":[214],"either.":[215]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-02T00:00:00"}
