{"id":"https://openalex.org/W7140144572","doi":"https://doi.org/10.18653/v1/2026.eacl-long.22","title":"Analogical Structure, Minimal Contextual Cues and Contrastive Distractors: Input Design for Sample-Efficient Linguistic Rule Induction","display_name":"Analogical Structure, Minimal Contextual Cues and Contrastive Distractors: Input Design for Sample-Efficient Linguistic Rule Induction","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7140144572","doi":"https://doi.org/10.18653/v1/2026.eacl-long.22"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.eacl-long.22","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.eacl-long.22","pdf_url":"https://aclanthology.org/2026.eacl-long.22.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 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.eacl-long.22.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Chunyang Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chunyang Jiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Paola Merlo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Paola Merlo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"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":0.40191294,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"485","last_page":"500"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.19740000367164612,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.19740000367164612,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.07029999792575836,"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/T10028","display_name":"Topic Modeling","score":0.04919999837875366,"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/rule-induction","display_name":"Rule induction","score":0.3224000036716461},{"id":"https://openalex.org/keywords/affect","display_name":"Affect (linguistics)","score":0.31529998779296875},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.26969999074935913},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.2619999945163727}],"concepts":[{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.4860000014305115},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4830999970436096},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.40790000557899475},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34389999508857727},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3280999958515167},{"id":"https://openalex.org/C2776780472","wikidata":"https://www.wikidata.org/wiki/Q7378945","display_name":"Rule induction","level":2,"score":0.3224000036716461},{"id":"https://openalex.org/C2776035688","wikidata":"https://www.wikidata.org/wiki/Q1606558","display_name":"Affect (linguistics)","level":2,"score":0.31529998779296875},{"id":"https://openalex.org/C46312422","wikidata":"https://www.wikidata.org/wiki/Q11024","display_name":"Communication","level":1,"score":0.28040000796318054},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2590000033378601}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.eacl-long.22","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.eacl-long.22","pdf_url":"https://aclanthology.org/2026.eacl-long.22.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 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.eacl-long.22","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.eacl-long.22","pdf_url":"https://aclanthology.org/2026.eacl-long.22.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 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.417558491230011}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320320924","display_name":"Schweizerischer Nationalfonds zur F\u00f6rderung der Wissenschaftlichen Forschung","ror":"https://ror.org/00yjd3n13"},{"id":"https://openalex.org/F4320323029","display_name":"Atomic Energy of Canada Limited","ror":"https://ror.org/019ja9e37"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7140144572.pdf","grobid_xml":"https://content.openalex.org/works/W7140144572.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2,76,129,174],"achieve":[3],"strong":[4],"performance":[5],"on":[6,58,78,91,120,152],"many":[7],"tasks,":[8],"but":[9],"their":[10,51],"training":[11],"makes":[12],"it":[13],"hard":[14],"to":[15,54,80],"see":[16],"which":[17],"properties":[18],"of":[19,32,56,103,163],"the":[20,59,85,100,121,127],"input":[21,33,158],"support":[22,35],"efficient":[23],"linguistic":[24,37,164],"rule":[25,38],"learning.We":[26],"ask":[27,49],"how":[28,50],"three":[29],"cognitively-inspired":[30],"principles":[31,65],"design":[34],"sample-efficient":[36,161],"induction:":[39],"analogical":[40,97],"structure,":[41],"contrastive":[42,107],"learning,":[43],"and":[44,106,109,166,175],"minimal":[45,110],"contextual":[46],"cue.We":[47],"also":[48,115],"effects":[52],"compare":[53],"those":[55],"LLMs":[57,119],"same":[60,122],"controlled":[61,125],"tasks.We":[62],"implement":[63],"these":[64,92],"in":[66],"structured":[67],"sentence":[68],"completion":[69],"tasks":[70],"that":[71,96,156],"test":[72],"English":[73],"verb":[74],"alternations.Lightweight":[75],"trained":[77,172],"hundreds":[79],"onethousand":[81],"such":[82],"examples":[83],"learn":[84],"alternation":[86],"rules":[87,165],"with":[88,133],"high":[89],"F1":[90,132],"tasks.Ablation":[93],"studies":[94],"show":[95,155],"organisation":[98,159],"is":[99],"main":[101],"driver":[102],"sample":[104],"efficiency,":[105],"distractors":[108],"context":[111],"help":[112],"further":[113],"gains.We":[114],"evaluate":[116],"zero-and":[117],"fewshot":[118],"tasks.In":[123],"this":[124,139],"setting,":[126],"lightweight":[128,173],"reach":[130],"higher":[131],"far":[134],"fewer":[135],"task-specific":[136],"data.We":[137],"treat":[138],"contrast":[140],"as":[141],"a":[142,149],"comparison":[143],"between":[144],"learning":[145,162,169],"regimes":[146],"rather":[147],"than":[148],"general":[150],"verdict":[151],"LLMs.Our":[153],"results":[154],"careful":[157],"supports":[160],"reveals":[167],"distinct":[168],"signatures":[170],"for":[171],"prompted":[176],"LLMs.":[177]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-02T00:00:00"}