{"id":"https://openalex.org/W7115562278","doi":"https://doi.org/10.48550/arxiv.2512.11013","title":"PIAST: Rapid Prompting with In-context Augmentation for Scarce Training data","display_name":"PIAST: Rapid Prompting with In-context Augmentation for Scarce Training data","publication_year":2025,"publication_date":"2025-12-11","ids":{"openalex":"https://openalex.org/W7115562278","doi":"https://doi.org/10.48550/arxiv.2512.11013"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2512.11013","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.11013","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2512.11013","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Batorski, Pawel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Batorski, Pawel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Swoboda, Paul","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Swoboda, Paul","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":null,"has_fulltext":false,"cited_by_count":0,"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/T13629","display_name":"Text Readability and Simplification","score":0.8497999906539917,"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/T13629","display_name":"Text Readability and Simplification","score":0.8497999906539917,"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":0.0215000007301569,"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":0.015699999406933784,"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/set","display_name":"Set (abstract data type)","score":0.6147000193595886},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.564300000667572},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5023000240325928},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.489300012588501},{"id":"https://openalex.org/keywords/lever","display_name":"Lever","score":0.46970000863075256},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.45419999957084656},{"id":"https://openalex.org/keywords/one-shot","display_name":"One shot","score":0.40290001034736633},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.3878999948501587}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7851999998092651},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6147000193595886},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.564300000667572},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5023000240325928},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.489300012588501},{"id":"https://openalex.org/C107524782","wikidata":"https://www.wikidata.org/wiki/Q40164","display_name":"Lever","level":2,"score":0.46970000863075256},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45820000767707825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4553000032901764},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.45419999957084656},{"id":"https://openalex.org/C2992734406","wikidata":"https://www.wikidata.org/wiki/Q413267","display_name":"One shot","level":2,"score":0.40290001034736633},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.3878999948501587},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.3806999921798706},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.36890000104904175},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.35089999437332153},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.34850001335144043},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3345000147819519},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32010000944137573},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2921000123023987},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.28870001435279846},{"id":"https://openalex.org/C112758219","wikidata":"https://www.wikidata.org/wiki/Q16038819","display_name":"Duration (music)","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2512.11013","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.11013","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2512.11013","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.11013","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"LLMs":[0],"are":[1,141],"highly":[2],"sensitive":[3],"to":[4],"prompt":[5,26,150],"design,":[6],"but":[7,107],"handcrafting":[8],"effective":[9],"prompts":[10],"is":[11,154],"difficult":[12],"and":[13,63,93,95,102,127,147],"often":[14],"requires":[15],"intricate":[16],"crafting":[17],"of":[18,38,53,117],"few-shot":[19,46],"examples.":[20,41],"We":[21],"propose":[22],"a":[23,35,64,81,114],"fast":[24,146],"automatic":[25,87,121],"construction":[27],"algorithm":[28],"that":[29,132],"augments":[30],"human":[31],"instructions":[32],"by":[33],"generating":[34],"small":[36],"set":[37,113],"few":[39],"shot":[40],"Our":[42,70,129,152],"method":[43,71],"iteratively":[44],"replaces/drops/keeps":[45],"examples":[47],"using":[48,75],"Monte":[49],"Carlo":[50],"Shapley":[51],"estimation":[52],"example":[54],"utility.":[55],"For":[56],"faster":[57,68],"execution,":[58],"we":[59,84,112],"use":[60],"aggressive":[61],"subsampling":[62],"replay":[65],"buffer":[66],"for":[67,145],"evaluations.":[69],"can":[72],"be":[73],"run":[74],"different":[76],"compute":[77,110],"time":[78],"budgets.":[79],"On":[80],"limited":[82],"budget,":[83],"outperform":[85],"existing":[86],"prompting":[88,122],"methods":[89,123],"on":[90,100,124],"text":[91],"simplification":[92,126],"GSM8K":[94],"obtain":[96],"second":[97],"best":[98],"results":[99,130],"classification":[101],"summarization.":[103],"With":[104],"an":[105],"extended,":[106],"still":[108],"modest":[109],"budget":[111],"new":[115],"state":[116],"the":[118,142],"art":[119],"among":[120],"classification,":[125],"GSM8K.":[128],"show":[131],"carefully":[133],"constructed":[134],"examples,":[135],"rather":[136],"than":[137],"exhaustive":[138],"instruction":[139],"search,":[140],"dominant":[143],"lever":[144],"data":[148],"efficient":[149],"engineering.":[151],"code":[153],"available":[155],"at":[156],"https://github.com/Batorskq/PIAST.":[157]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-12-16T00:00:00"}