{"id":"https://openalex.org/W4389520255","doi":"https://doi.org/10.18653/v1/2023.findings-emnlp.679","title":"Demystifying Prompts in Language Models via Perplexity Estimation","display_name":"Demystifying Prompts in Language Models via Perplexity Estimation","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4389520255","doi":"https://doi.org/10.18653/v1/2023.findings-emnlp.679"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2023.findings-emnlp.679","doi":"https://doi.org/10.18653/v1/2023.findings-emnlp.679","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2023.findings-emnlp.679","pdf_url":"https://aclanthology.org/2023.findings-emnlp.679.pdf","source":{"id":"https://openalex.org/S7407096085","display_name":"Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320244","host_organization_name":"Association for Computational Linguistics","host_organization_lineage":["https://openalex.org/P4310320244"],"host_organization_lineage_names":["Association for Computational Linguistics"],"type":"conference"},"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: EMNLP 2023","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://aclanthology.org/2023.findings-emnlp.679.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5083184681","display_name":"Hila Gonen","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hila Gonen","raw_affiliation_strings":["Meta AI Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meta AI Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088004913","display_name":"Srinivasan V. Iyer","orcid":"https://orcid.org/0000-0002-6186-2603","observed_orcids":["https://orcid.org/0000-0002-6186-2603"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Srini Iyer","raw_affiliation_strings":["Meta AI Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meta AI Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088311840","display_name":"Terra Blevins","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Terra Blevins","raw_affiliation_strings":["School of Computer Science & Engineering, University of Washington"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science & Engineering, University of Washington","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088517824","display_name":"Noah A. Smith","orcid":"https://orcid.org/0000-0002-2310-6380","observed_orcids":["https://orcid.org/0000-0002-2310-6380"]},"institutions":[{"id":"https://openalex.org/I4210156221","display_name":"Allen Institute for Artificial Intelligence","ror":"https://ror.org/05w520734","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210156221"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Noah Smith","raw_affiliation_strings":["Allen Institute for Artificial Intelligence"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Allen Institute for Artificial Intelligence","institution_ids":["https://openalex.org/I4210156221"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067919401","display_name":"Luke Zettlemoyer","orcid":"https://orcid.org/0009-0008-8296-0764","observed_orcids":["https://orcid.org/0009-0008-8296-0764"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luke Zettlemoyer","raw_affiliation_strings":["Meta AI Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meta AI Research","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5088004913"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":19.8337,"has_fulltext":true,"cited_by_count":126,"citation_normalized_percentile":{"value":0.99620276,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"10136","last_page":"10148"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.8809000253677368,"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":0.8809000253677368,"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":0.09229999780654907,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.019999999552965164,"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/prompt-engineering","display_name":"prompt engineering","score":0.6959999799728394},{"id":"https://openalex.org/keywords/language-models","display_name":"language models","score":0.6650000214576721},{"id":"https://openalex.org/keywords/in-context-learning","display_name":"in-context learning","score":0.9269999861717224},{"id":"https://openalex.org/keywords/zero-shot-learning","display_name":"zero-shot learning","score":0.7900000214576721},{"id":"https://openalex.org/keywords/few-shot-learning","display_name":"few-shot learning","score":0.9860000014305115},{"id":"https://openalex.org/keywords/paraphrasing","display_name":"paraphrase","score":0.6539999842643738},{"id":"https://openalex.org/keywords/back-translation","display_name":"back-translation","score":0.8169999718666077}],"concepts":[{"id":"https://openalex.org/C100279451","wikidata":"https://www.wikidata.org/wiki/Q372193","display_name":"Perplexity","level":3,"score":0.9968765377998352},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8432126045227051},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.6763049364089966},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6508232355117798},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.6457152962684631},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.615993320941925},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6158865690231323},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.5916778445243835},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5508822798728943},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5405269265174866},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4541347622871399},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4102838635444641},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.06842419505119324},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","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/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/v1/2023.findings-emnlp.679","doi":"https://doi.org/10.18653/v1/2023.findings-emnlp.679","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2023.findings-emnlp.679","pdf_url":"https://aclanthology.org/2023.findings-emnlp.679.pdf","source":{"id":"https://openalex.org/S7407096085","display_name":"Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320244","host_organization_name":"Association for Computational Linguistics","host_organization_lineage":["https://openalex.org/P4310320244"],"host_organization_lineage_names":["Association for Computational Linguistics"],"type":"conference"},"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: EMNLP 2023","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2212.04037","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2212.04037","pdf_url":"https://arxiv.org/pdf/2212.04037","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2212.04037","doi":"https://doi.org/10.48550/arxiv.2212.04037","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2212.04037","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"}],"best_oa_location":{"id":"doi:10.18653/v1/2023.findings-emnlp.679","doi":"https://doi.org/10.18653/v1/2023.findings-emnlp.679","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2023.findings-emnlp.679","pdf_url":"https://aclanthology.org/2023.findings-emnlp.679.pdf","source":{"id":"https://openalex.org/S7407096085","display_name":"Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320244","host_organization_name":"Association for Computational Linguistics","host_organization_lineage":["https://openalex.org/P4310320244"],"host_organization_lineage_names":["Association for Computational Linguistics"],"type":"conference"},"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: EMNLP 2023","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4389520255.pdf","grobid_xml":"https://content.openalex.org/works/W4389520255.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W1552847225","https://openalex.org/W2113459411","https://openalex.org/W2170240176","https://openalex.org/W2784720878","https://openalex.org/W2806120502","https://openalex.org/W2891575196","https://openalex.org/W2963001247","https://openalex.org/W2993818995","https://openalex.org/W3030163527","https://openalex.org/W3085177480","https://openalex.org/W3094002940","https://openalex.org/W3098267758","https://openalex.org/W3135934234","https://openalex.org/W3152956381","https://openalex.org/W3157005959","https://openalex.org/W3166846774","https://openalex.org/W3173777717","https://openalex.org/W3174770825","https://openalex.org/W3198963017","https://openalex.org/W4221151371","https://openalex.org/W4229005866","https://openalex.org/W4281784180","https://openalex.org/W4285077564","https://openalex.org/W4285178342","https://openalex.org/W4287891033","https://openalex.org/W4288804650","https://openalex.org/W4309217888","https://openalex.org/W4385573003","https://openalex.org/W4386506836","https://openalex.org/W4389518888","https://openalex.org/W4389519967","https://openalex.org/W7000218544"],"related_works":["https://openalex.org/W4389520436","https://openalex.org/W4404781875","https://openalex.org/W4399794972","https://openalex.org/W2991382858","https://openalex.org/W3198599617","https://openalex.org/W4303648559","https://openalex.org/W4404792934","https://openalex.org/W4404781496","https://openalex.org/W3156470785","https://openalex.org/W7162763763"],"abstract_inverted_index":{"Language":[0],"models":[1],"can":[2],"be":[3],"prompted":[4],"to":[5,42,61,92,103,114,135],"perform":[6,93],"a":[7,47,54,72,112,117,140,147],"wide":[8,73],"variety":[9],"of":[10,23,53,75,84,106,121,143,146],"tasks":[11],"with":[12,20,67,127],"zero-and":[13],"few-shot":[14],"incontext":[15],"learning.However,":[16],"performance":[17,52],"varies":[18],"significantly":[19],"the":[21,38,51,59,63,68,80,82,85,87,94,144,153],"choice":[22],"prompt,":[24,86],"and":[25,45,129,149],"we":[26,36,77,109,150],"do":[27],"not":[28],"yet":[29],"understand":[30],"why":[31],"this":[32,34,43],"happens.In":[33],"paper,":[35],"analyze":[37],"factors":[39],"that":[40,79,100,137,152],"contribute":[41],"variance":[44],"establish":[46],"new":[48],"empirical":[49],"hypothesis:":[50],"prompt":[55,148],"is":[56,65,90,139],"predicted":[57],"by":[58,125],"extent":[60],"which":[62],"model":[64],"familiar":[66],"language":[69],"it":[70,89],"contains.Over":[71],"range":[74],"tasks,":[76],"show":[78,151],"lower":[81],"perplexity":[83,138,155],"better":[88],"able":[91],"task,":[95],"when":[96],"considering":[97],"reasonable":[98],"prompts":[99,124,156],"are":[101,157],"related":[102],"it.As":[104],"part":[105],"our":[107],"analysis,":[108],"also":[110],"devise":[111],"method":[113],"automatically":[115],"extend":[116],"small":[118],"seed":[119],"set":[120,132],"manually":[122],"written":[123],"paraphrasing":[126],"GPT3":[128],"backtranslation.This":[130],"larger":[131],"allows":[133],"us":[134],"verify":[136],"strong":[141],"predictor":[142],"success":[145],"lowest":[154],"consistently":[158],"effective.":[159]},"counts_by_year":[{"year":2026,"cited_by_count":16},{"year":2025,"cited_by_count":39},{"year":2024,"cited_by_count":39},{"year":2023,"cited_by_count":31},{"year":2022,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2023-12-11T00:00:00"}