{"id":"https://openalex.org/W7161978468","doi":"https://doi.org/10.48550/arxiv.2605.21318","title":"TextReg: Mitigating Prompt Distributional Overfitting via Regularized Text-Space Optimization","display_name":"TextReg: Mitigating Prompt Distributional Overfitting via Regularized Text-Space Optimization","publication_year":2026,"publication_date":"2026-05-20","ids":{"openalex":"https://openalex.org/W7161978468","doi":"https://doi.org/10.48550/arxiv.2605.21318"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.21318","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21318","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.21318","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5083272046","display_name":"\u5085\u5362\u6210","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu, Lucheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136697856","display_name":"Ye Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Ye","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136640419","display_name":"Yiyang Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yiyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136689829","display_name":"Yiqiao Jin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Yiqiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136656708","display_name":"Haibo Jin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Haibo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136698926","display_name":"B. Aditya Prakash","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prakash, B. Aditya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136721952","display_name":"Haohan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Haohan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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/T10028","display_name":"Topic Modeling","score":0.41110000014305115,"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":0.41110000014305115,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.07150000333786011,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.05550000071525574,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/overfitting","display_name":"Overfitting","score":0.9320999979972839},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.6317999958992004},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5031999945640564},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.477400004863739},{"id":"https://openalex.org/keywords/inefficiency","display_name":"Inefficiency","score":0.45419999957084656},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4307999908924103},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.36629998683929443},{"id":"https://openalex.org/keywords/scope","display_name":"Scope (computer science)","score":0.362199991941452}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.9320999979972839},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6819000244140625},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6514999866485596},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6317999958992004},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6025999784469604},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5031999945640564},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.477400004863739},{"id":"https://openalex.org/C2778869765","wikidata":"https://www.wikidata.org/wiki/Q6028363","display_name":"Inefficiency","level":2,"score":0.45419999957084656},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4307999908924103},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.36629998683929443},{"id":"https://openalex.org/C2778012447","wikidata":"https://www.wikidata.org/wiki/Q1034415","display_name":"Scope (computer science)","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.3224000036716461},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3197000026702881},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3142000138759613},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3075000047683716},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2806999981403351},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.26429998874664307},{"id":"https://openalex.org/C132459708","wikidata":"https://www.wikidata.org/wiki/Q744069","display_name":"Extrapolation","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.21318","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21318","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.21318","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21318","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":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.45573943853378296,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2],"(LLMs)":[3],"are":[4],"highly":[5],"sensitive":[6],"to":[7,11,94,141],"the":[8,30,44],"prompts":[9,25,32],"used":[10],"specify":[12],"task":[13],"objectives":[14],"and":[15,40,56,87,122,145],"behavioral":[16],"constraints.":[17],"Many":[18],"recent":[19],"prompt":[20,53,82,92],"optimization":[21],"methods":[22],"iteratively":[23],"rewrite":[24],"using":[26],"LLM-generated":[27],"feedback,":[28],"but":[29],"resulting":[31],"often":[33],"become":[34],"longer,":[35],"accumulate":[36],"narrow":[37],"sample-specific":[38],"rules,":[39],"generalize":[41],"poorly":[42],"beyond":[43],"training":[45],"distribution.":[46],"We":[47,70,100],"study":[48],"this":[49,72],"failure":[50],"mode":[51],"as":[52],"distributional":[54,91],"overfitting":[55,93],"argue":[57],"that":[58,80,106],"it":[59],"reflects":[60],"a":[61,77,103,108],"lack":[62],"of":[63,139],"representation":[64],"control":[65],"in":[66],"discrete":[67],"text-space":[68],"optimization.":[69,99],"formalize":[71],"view":[73],"through":[74,111],"representational":[75],"inefficiency,":[76],"dual-factor":[78],"measure":[79],"decomposes":[81],"inefficiency":[83],"into":[84],"capacity":[85],"cost":[86],"scope":[88],"narrowness,":[89],"attributing":[90],"their":[95],"coupled":[96],"growth":[97],"during":[98],"propose":[101],"TextReg,":[102],"regularization":[104],"framework":[105],"realizes":[107],"soft-penalty":[109],"objective":[110],"regularized":[112],"textual":[113],"gradients,":[114],"combining":[115],"Dual-Evidence":[116],"Gradient":[117],"Purification,":[118],"Semantic":[119],"Edit":[120],"Regularization,":[121],"Regularization-Guided":[123],"Prompt":[124],"Update.":[125],"Across":[126],"multiple":[127],"reasoning":[128],"benchmarks,":[129],"TextReg":[130],"substantially":[131],"improves":[132],"out-of-distribution":[133],"(OOD)":[134],"generalization,":[135],"with":[136],"accuracy":[137],"gains":[138],"up":[140],"+11.8%":[142],"over":[143,147],"TextGrad":[144],"+16.5%":[146],"REVOLVE.":[148]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-22T00:00:00"}