{"id":"https://openalex.org/W4391212816","doi":"https://doi.org/10.48550/arxiv.2401.12973","title":"In-Context Language Learning: Architectures and Algorithms","display_name":"In-Context Language Learning: Architectures and Algorithms","publication_year":2024,"publication_date":"2024-01-23","ids":{"openalex":"https://openalex.org/W4391212816","doi":"https://doi.org/10.48550/arxiv.2401.12973"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2401.12973","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.12973","pdf_url":"https://arxiv.org/pdf/2401.12973","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2401.12973","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071734030","display_name":"Ekin Aky\u00fcrek","orcid":"https://orcid.org/0000-0002-5166-4689"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aky\u00fcrek, Ekin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042749062","display_name":"Bailin Wang","orcid":"https://orcid.org/0000-0002-3579-298X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Bailin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100693798","display_name":"Yoon Kim","orcid":"https://orcid.org/0000-0002-4837-8411"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Yoon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5070829652","display_name":"Jacob Andreas","orcid":"https://orcid.org/0000-0002-3141-5845"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andreas, Jacob","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9994000196456909,"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":0.9994000196456909,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9993000030517578,"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":0.9846000075340271,"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/computer-science","display_name":"Computer science","score":0.8002731800079346},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.6335809826850891},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6018351316452026},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.49869680404663086},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.47836416959762573},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41311943531036377},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4084811508655548},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3943401277065277},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3598504066467285}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8002731800079346},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.6335809826850891},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6018351316452026},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.49869680404663086},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.47836416959762573},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41311943531036377},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4084811508655548},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3943401277065277},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3598504066467285},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2401.12973","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.12973","pdf_url":"https://arxiv.org/pdf/2401.12973","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2401.12973","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2401.12973","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2401.12973","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.12973","pdf_url":"https://arxiv.org/pdf/2401.12973","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.800000011920929}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4391212816.pdf","grobid_xml":"https://content.openalex.org/works/W4391212816.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4288365749","https://openalex.org/W2936497627","https://openalex.org/W3013624417","https://openalex.org/W4287826556","https://openalex.org/W3098382480","https://openalex.org/W4287598411","https://openalex.org/W3094871513","https://openalex.org/W3100913109","https://openalex.org/W3198458223","https://openalex.org/W3126642501"],"abstract_inverted_index":{"Large-scale":[0],"neural":[1,151,209,253],"language":[2,81,106,263],"models":[3,153,186,211,254,271],"exhibit":[4],"a":[5,50,96,115,120,147],"remarkable":[6],"capacity":[7],"for":[8],"in-context":[9,135],"learning":[10,40,107,136],"(ICL):":[11],"they":[12],"can":[13,195],"infer":[14],"novel":[15],"functions":[16],"from":[17,34,119,128],"datasets":[18],"provided":[19],"as":[20],"input.":[21],"Most":[22],"of":[23,27,80,95,99,117,137,150,178,237,269],"our":[24],"current":[25],"understanding":[26],"when":[28],"and":[29,45,57,74,82,123,158],"how":[30],"ICL":[31,60,91],"arises":[32],"comes":[33],"LMs":[35,63,111],"trained":[36,64],"on":[37,65,134,162,217,231,259,278],"extremely":[38],"simple":[39],"problems":[41,56,101],"like":[42],"linear":[43],"regression":[44],"associative":[46],"recall.":[47],"There":[48],"remains":[49],"significant":[51],"gap":[52],"between":[53],"these":[54,250],"model":[55,100,160,173],"the":[58,93,129,267,279],"\"real\"":[59],"exhibited":[61],"by":[62,141,272],"large":[66],"text":[67],"corpora,":[68],"which":[69],"involves":[70],"not":[71,257],"just":[72,258],"retrieval":[73],"function":[75],"approximation":[76],"but":[77,261],"free-form":[78],"generation":[79],"other":[83],"structured":[84],"outputs.":[85],"In":[86,109],"this":[87],"paper,":[88],"we":[89,102,221,246],"study":[90],"through":[92],"lens":[94],"new":[97],"family":[98],"term":[103],"in":[104,198],"context":[105],"(ICLL).":[108],"ICLL,":[110,260],"are":[112,175],"presented":[113],"with":[114,212],"set":[116,149],"strings":[118,127],"formal":[121],"language,":[122],"must":[124],"generate":[125],"additional":[126],"same":[130],"language.":[131],"We":[132,145,202],"focus":[133],"regular":[138,163],"languages":[139],"generated":[140],"random":[142],"finite":[143],"automata.":[144],"evaluate":[146],"diverse":[148],"sequence":[152,210],"(including":[154],"several":[155],"RNNs,":[156],"Transformers,":[157],"state-space":[159],"variants)":[161],"ICLL":[164,197,218],"tasks,":[165],"aiming":[166],"to":[167,188,227,274],"answer":[168],"three":[169],"questions:":[170],"(1)":[171],"Which":[172],"classes":[174],"empirically":[176],"capable":[177],"ICLL?":[179,190],"(2)":[180],"What":[181,192],"algorithmic":[182],"solutions":[183],"do":[184,228],"successful":[185],"implement":[187],"perform":[189],"(3)":[191],"architectural":[193],"changes":[194],"improve":[196],"less":[199],"performant":[200],"models?":[201],"first":[203],"show":[204,247],"that":[205,224,240,248],"Transformers":[206],"significantly":[207],"outperform":[208],"recurrent":[213],"or":[214],"convolutional":[215],"representations":[216],"tasks.":[219],"Next,":[220],"provide":[222],"evidence":[223],"their":[225],"ability":[226],"so":[229],"relies":[230],"specialized":[232],"\"n-gram":[233],"heads\"":[234],"(higher-order":[235],"variants":[236],"induction":[238],"heads)":[239],"compute":[241],"input-conditional":[242],"next-token":[243],"distributions.":[244],"Finally,":[245],"hard-wiring":[249],"heads":[251],"into":[252],"improves":[255],"performance":[256],"natural":[262],"modeling":[264],"--":[265],"improving":[266],"perplexity":[268],"340M-parameter":[270],"up":[273],"1.14":[275],"points":[276],"(6.7%)":[277],"SlimPajama":[280],"dataset.":[281]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2024-01-25T00:00:00"}