{"id":"https://openalex.org/W4412888595","doi":"https://doi.org/10.18653/v1/2025.findings-acl.345","title":"Learning Task Representations from In-Context Learning","display_name":"Learning Task Representations from In-Context Learning","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412888595","doi":"https://doi.org/10.18653/v1/2025.findings-acl.345"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2025.findings-acl.345","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-acl.345","pdf_url":"https://aclanthology.org/2025.findings-acl.345.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: ACL 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-acl.345.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040880684","display_name":"Baturay Sa\u011flam","orcid":"https://orcid.org/0000-0002-8324-5980"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Baturay Saglam","raw_affiliation_strings":["Yale University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101242111","display_name":"Xinyang Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xinyang Hu","raw_affiliation_strings":["Yale University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101727948","display_name":"Zhuoran Yang","orcid":"https://orcid.org/0000-0001-5269-9958"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhuoran Yang","raw_affiliation_strings":["Yale University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091493591","display_name":"Dionysios S. Kalogerias","orcid":"https://orcid.org/0000-0002-3459-5044"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dionysis Kalogerias","raw_affiliation_strings":["Yale University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5098577430","display_name":"Amin Karbasi","orcid":null},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amin Karbasi","raw_affiliation_strings":["Yale University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University","institution_ids":["https://openalex.org/I32971472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I32971472"],"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":"6634","last_page":"6663"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.9652000069618225,"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/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.9652000069618225,"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/T11122","display_name":"Online Learning and Analytics","score":0.9200000166893005,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7273285388946533},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6427099704742432},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5475736856460571},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.42789697647094727},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4217260181903839},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.41582798957824707},{"id":"https://openalex.org/keywords/cognitive-psychology","display_name":"Cognitive psychology","score":0.3846886456012726},{"id":"https://openalex.org/keywords/cognitive-science","display_name":"Cognitive science","score":0.3613409996032715},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.23530566692352295},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08241605758666992}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7273285388946533},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6427099704742432},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5475736856460571},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.42789697647094727},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4217260181903839},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.41582798957824707},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.3846886456012726},{"id":"https://openalex.org/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","level":1,"score":0.3613409996032715},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.23530566692352295},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08241605758666992},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.findings-acl.345","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-acl.345","pdf_url":"https://aclanthology.org/2025.findings-acl.345.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: ACL 2025","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.findings-acl.345","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-acl.345","pdf_url":"https://aclanthology.org/2025.findings-acl.345.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: ACL 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.4099999964237213,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412888595.pdf","grobid_xml":"https://content.openalex.org/works/W4412888595.grobid-xml"},"referenced_works_count":1,"referenced_works":["https://openalex.org/W4230872509"],"related_works":["https://openalex.org/W2803309984","https://openalex.org/W2963872552","https://openalex.org/W2237537322","https://openalex.org/W4301248618","https://openalex.org/W2950678851","https://openalex.org/W2914746235","https://openalex.org/W3196817267","https://openalex.org/W1976600725","https://openalex.org/W4211075255","https://openalex.org/W2951720331"],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2,13],"(LLMs)":[3],"have":[4],"demonstrated":[5],"remarkable":[6],"proficiency":[7],"in":[8,44,56,119,134],"in-context":[9,130],"learning":[10],"(ICL),":[11],"where":[12],"adapt":[14],"to":[15,96,99,109],"new":[16],"tasks":[17,27],"through":[18],"example-based":[19],"prompts":[20,58],"without":[21],"requiring":[22],"parameter":[23],"updates.However,":[24],"understanding":[25],"how":[26],"are":[28],"internally":[29],"encoded":[30],"and":[31,41,132,137],"generalized":[32],"remains":[33],"a":[34,60,71,76,107,112],"challenge.To":[35],"address":[36],"some":[37],"of":[38,62,79],"the":[39,45,66,83],"empirical":[40],"technical":[42],"gaps":[43],"literature,":[46],"we":[47,104],"introduce":[48],"an":[49],"automated":[50],"formulation":[51],"for":[52],"encoding":[53],"task":[54,73,113,117],"information":[55,128],"ICL":[57],"as":[59,75],"function":[61],"attention":[63,80],"heads":[64],"within":[65],"transformer":[67],"architecture.This":[68],"approach":[69],"computes":[70],"single":[72],"vector":[74,114],"weighted":[77],"sum":[78],"heads,":[81],"with":[82],"weights":[84],"optimized":[85],"causally":[86],"via":[87],"gradient":[88],"descent.Our":[89],"findings":[90],"show":[91],"that":[92],"existing":[93],"methods":[94],"fail":[95],"generalize":[97],"effectively":[98],"modalities":[100],"beyond":[101],"text.In":[102],"response,":[103],"also":[105],"design":[106],"benchmark":[108],"evaluate":[110],"whether":[111],"can":[115],"preserve":[116],"fidelity":[118],"functional":[120],"regression":[121,138],"tasks.The":[122],"proposed":[123],"method":[124],"successfully":[125],"extracts":[126],"task-specific":[127],"from":[129],"demonstrations":[131],"excels":[133],"both":[135],"text":[136],"tasks,":[139],"demonstrating":[140],"its":[141],"generalizability":[142],"across":[143],"modalities.":[144]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
