{"id":"https://openalex.org/W7160320543","doi":"https://doi.org/10.48550/arxiv.2605.01311","title":"The Partial Testimony of Logs: Evaluation of Language Model Generation under Confounded Model Choice","display_name":"The Partial Testimony of Logs: Evaluation of Language Model Generation under Confounded Model Choice","publication_year":2026,"publication_date":"2026-05-02","ids":{"openalex":"https://openalex.org/W7160320543","doi":"https://doi.org/10.48550/arxiv.2605.01311"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.01311","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01311","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.2605.01311","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135325313","display_name":"Jikai Jin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Jikai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5112443698","display_name":"Vasilis Syrgkanis","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Syrgkanis, Vasilis","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":0.21299999952316284,"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":0.21299999952316284,"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":0.09589999914169312,"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/T12127","display_name":"Software System Performance and Reliability","score":0.07739999890327454,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/estimator","display_name":"Estimator","score":0.617900013923645},{"id":"https://openalex.org/keywords/observational-study","display_name":"Observational study","score":0.5899999737739563},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5629000067710876},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5091000199317932},{"id":"https://openalex.org/keywords/randomized-experiment","display_name":"Randomized experiment","score":0.5076000094413757},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.43959999084472656},{"id":"https://openalex.org/keywords/automatic-summarization","display_name":"Automatic summarization","score":0.42320001125335693},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.40939998626708984}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6711999773979187},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.617900013923645},{"id":"https://openalex.org/C23131810","wikidata":"https://www.wikidata.org/wiki/Q818574","display_name":"Observational study","level":2,"score":0.5899999737739563},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5629000067710876},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5091000199317932},{"id":"https://openalex.org/C155108698","wikidata":"https://www.wikidata.org/wiki/Q1231081","display_name":"Randomized experiment","level":2,"score":0.5076000094413757},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4397999942302704},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.43959999084472656},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.43320000171661377},{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.42320001125335693},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.40939998626708984},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40869998931884766},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4025000035762787},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3977000117301941},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35260000824928284},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3257000148296356},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.3172000050544739},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.30820000171661377},{"id":"https://openalex.org/C115537543","wikidata":"https://www.wikidata.org/wiki/Q165596","display_name":"Cache","level":2,"score":0.30379998683929443},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2962000072002411},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C40696583","wikidata":"https://www.wikidata.org/wiki/Q989120","display_name":"Type I and type II errors","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.2678000032901764},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.2574999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.01311","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01311","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.2605.01311","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01311","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Offline":[0],"evaluation":[1],"of":[2,36,48,178],"language":[3],"models":[4,102],"from":[5],"usage":[6],"logs":[7],"is":[8,13,23,31,109],"biased":[9],"when":[10],"model":[11,22,60,127],"choice":[12],"confounded:":[14],"the":[15,115,119,129,143,176,186],"same":[16],"user-side":[17],"factors":[18],"that":[19,76,99,114],"influence":[20,27],"which":[21],"used":[24],"can":[25,54],"also":[26],"how":[28,184],"its":[29],"output":[30],"judged,":[32],"so":[33],"raw":[34],"comparisons":[35],"logged":[37],"scores":[38],"mix":[39],"self-selected":[40],"populations":[41],"rather":[42,139],"than":[43,140],"estimating":[44],"a":[45,73,78,86,153],"common":[46],"quantity":[47],"interest.":[49],"A":[50],"small":[51,87],"randomized":[52,88,116],"experiment":[53,89,117],"break":[55],"this":[56],"bias":[57],"by":[58],"overriding":[59],"choice,":[61],"but":[62],"in":[63,152,158],"practice":[64],"such":[65],"experiments":[66],"are":[67,121,150],"scarce":[68],"and":[69,94,118,157,165,182],"costly.":[70],"We":[71],"study":[72],"three-source":[74],"design":[75],"combines":[77],"large":[79],"confounded":[80],"observational":[81,130],"log":[82,131],"(OBS)":[83],"for":[84,91,163],"scale,":[85],"(EXP)":[90],"unconfounded":[92],"scoring,":[93],"an":[95,110],"offline":[96],"simulator":[97,120],"(SIM)":[98],"replays":[100],"candidate":[101],"on":[103,175,183],"cached":[104,161],"contexts.":[105],"Our":[106],"main":[107],"result":[108],"identification":[111],"theorem":[112],"showing":[113],"together":[122],"enough":[123],"to":[124,135,141],"recover":[125],"causal":[126,144],"values;":[128],"enters":[132],"only":[133],"afterward,":[134],"reduce":[136],"estimation":[137],"error":[138],"make":[142],"comparison":[145],"valid.":[146],"Six":[147],"estimator":[148],"families":[149],"evaluated":[151],"controlled":[154],"semi-synthetic":[155],"validation":[156],"two":[159],"real-task":[160],"benchmarks":[162],"summarization":[164],"coding.":[166],"No":[167],"family":[168],"dominates":[169],"every":[170],"regime;":[171],"relative":[172],"performance":[173],"depends":[174],"amount":[177],"unbiased":[179],"EXP":[180],"supervision":[181],"closely":[185],"target":[187],"reward":[188],"aligns":[189],"with":[190],"OBS-derived":[191],"structure.":[192]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-06T00:00:00"}
