{"id":"https://openalex.org/W7151332048","doi":"https://doi.org/10.48550/arxiv.2604.03684","title":"Researchers waste 80% of LLM annotation costs by classifying one text at a time","display_name":"Researchers waste 80% of LLM annotation costs by classifying one text at a time","publication_year":2026,"publication_date":"2026-04-04","ids":{"openalex":"https://openalex.org/W7151332048","doi":"https://doi.org/10.48550/arxiv.2604.03684"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.03684","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03684","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.2604.03684","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034464160","display_name":"Christian Pipal","orcid":"https://orcid.org/0000-0002-5395-2035"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Pipal, Christian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014527442","display_name":"Eva-Maria Vogel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vogel, Eva-Maria","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017612757","display_name":"Morgan Wack","orcid":"https://orcid.org/0000-0002-7769-5993"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wack, Morgan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5122500786","display_name":"Frank Esser","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Esser, Frank","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5034464160"],"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/T13910","display_name":"Computational and Text Analysis Methods","score":0.7871999740600586,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.7871999740600586,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12380","display_name":"Authorship Attribution and Profiling","score":0.023800000548362732,"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":0.022299999371170998,"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/coding","display_name":"Coding (social sciences)","score":0.7028999924659729},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.5472999811172485},{"id":"https://openalex.org/keywords/stacking","display_name":"Stacking","score":0.5461999773979187},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5098000168800354},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.4814000129699707},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4065000116825104},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.3702999949455261}],"concepts":[{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.7028999924659729},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6553999781608582},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5472999811172485},{"id":"https://openalex.org/C33347731","wikidata":"https://www.wikidata.org/wiki/Q285210","display_name":"Stacking","level":2,"score":0.5461999773979187},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5098000168800354},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.4814000129699707},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4065000116825104},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3714999854564667},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.3702999949455261},{"id":"https://openalex.org/C27574286","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Variables","level":2,"score":0.36390000581741333},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.35850000381469727},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.35269999504089355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3116999864578247},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.30959999561309814},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3091000020503998},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27959999442100525},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.03684","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03684","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.2604.03684","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03684","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":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5964862704277039}],"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":[2,99],"(LLMs)":[3],"are":[4],"increasingly":[5],"being":[6],"used":[7],"for":[8],"text":[9,20],"classification":[10],"across":[11,76],"the":[12,106,142,156],"social":[13],"sciences,":[14],"yet":[15],"researchers":[16],"overwhelmingly":[17],"classify":[18],"one":[19],"per":[21,23,94],"variable":[22],"prompt.":[24,95],"Coding":[25],"100,000":[26],"texts":[27],"on":[28,72],"four":[29,70,77],"variables":[30,41],"requires":[31],"400,000":[32],"API":[33],"calls.":[34],"Batching":[35],"25":[36,91],"items":[37,86],"and":[38,87,147],"stacking":[39,88,115,148],"all":[40],"into":[42],"a":[43],"single":[44],"prompt":[45,135],"reduces":[46],"this":[47,58,138],"to":[48,84,90,118,124],"4,000":[49],"calls,":[50],"cutting":[51],"token":[52],"costs":[53],"by":[54,130],"over":[55],"80%.":[56],"Whether":[57],"degrades":[59],"coding":[60,92],"quality":[61],"is":[62,149],"unknown.":[63],"We":[64],"tested":[65],"eight":[66,98],"production":[67],"LLMs":[68],"from":[69,82,145],"providers":[71],"3,962":[73],"expert-coded":[74],"tweets":[75],"tasks,":[78],"varying":[79],"batch":[80,110],"size":[81],"1":[83],"1,000":[85],"up":[89,117],"dimensions":[93,120],"Six":[96],"of":[97,105,112],"maintained":[100],"accuracy":[101],"within":[102],"2":[103],"pp":[104],"single-item":[107],"baseline":[108],"through":[109],"sizes":[111],"100.":[113],"Variable":[114],"with":[116,127],"10":[119],"produced":[121],"results":[122],"comparable":[123],"single-variable":[125],"coding,":[126],"degradation":[128],"driven":[129],"task":[131],"complexity":[132],"rather":[133],"than":[134,151],"length.":[136],"Within":[137],"safe":[139],"operating":[140],"range,":[141],"measurement":[143],"error":[144],"batching":[146],"smaller":[150],"typical":[152],"inter-coder":[153],"disagreement":[154],"in":[155],"ground-truth":[157],"data.":[158]},"counts_by_year":[],"updated_date":"2026-04-08T06:07:18.267832","created_date":"2026-04-08T00:00:00"}