{"id":"https://openalex.org/W7162142677","doi":"https://doi.org/10.48550/arxiv.2605.22176","title":"LLM-Metrics: Measuring Research Impact Through Large Language Model Memory","display_name":"LLM-Metrics: Measuring Research Impact Through Large Language Model Memory","publication_year":2026,"publication_date":"2026-05-21","ids":{"openalex":"https://openalex.org/W7162142677","doi":"https://doi.org/10.48550/arxiv.2605.22176"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.22176","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22176","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.2605.22176","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136740962","display_name":"Si Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Si","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136773386","display_name":"Wenhua Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Wenhua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5054149472","display_name":"Danhao Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Danhao","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/T10102","display_name":"scientometrics and bibliometrics research","score":0.5157999992370605,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10102","display_name":"scientometrics and bibliometrics research","score":0.5157999992370605,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.1307000070810318,"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/T13607","display_name":"Academic Publishing and Open Access","score":0.08619999885559082,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.7283999919891357},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6930000185966492},{"id":"https://openalex.org/keywords/citation","display_name":"Citation","score":0.64410001039505},{"id":"https://openalex.org/keywords/predictive-power","display_name":"Predictive power","score":0.5421000123023987},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.5288000106811523},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.3982999920845032},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.3831999897956848},{"id":"https://openalex.org/keywords/parametric-model","display_name":"Parametric model","score":0.3626999855041504},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.3492000102996826}],"concepts":[{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.7283999919891357},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6930000185966492},{"id":"https://openalex.org/C2778805511","wikidata":"https://www.wikidata.org/wiki/Q1713","display_name":"Citation","level":2,"score":0.64410001039505},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5422999858856201},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.5421000123023987},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5288000106811523},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42640000581741333},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.4025000035762787},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.3982999920845032},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39340001344680786},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3831999897956848},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3781000077724457},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.3626999855041504},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3521000146865845},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.3492000102996826},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.3449999988079071},{"id":"https://openalex.org/C8795937","wikidata":"https://www.wikidata.org/wiki/Q11862829","display_name":"Discipline","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3208000063896179},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2930999994277954},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C2780813799","wikidata":"https://www.wikidata.org/wiki/Q3274237","display_name":"Zero (linguistics)","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C96608239","wikidata":"https://www.wikidata.org/wiki/Q1199823","display_name":"Statistical power","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.26510000228881836},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.2648000121116638},{"id":"https://openalex.org/C3020318244","wikidata":"https://www.wikidata.org/wiki/Q4812187","display_name":"Large sample","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C2778793908","wikidata":"https://www.wikidata.org/wiki/Q5122404","display_name":"Citation impact","level":3,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.22176","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22176","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.2605.22176","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22176","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/10","display_name":"Reduced inequalities","score":0.6970770359039307}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Citation":[0],"counts":[1,168],"remain":[2],"the":[3,33,52,114,151,155,176,187,223],"dominant":[4],"metric":[5,30],"for":[6,160,243],"assessing":[7],"research":[8,244],"impact,":[9],"yet":[10],"they":[11],"suffer":[12],"from":[13,32,110],"well-documented":[14],"limitations:":[15],"temporal":[16],"lag,":[17],"disciplinary":[18],"bias,":[19],"and":[20,65,90,93,140,200],"Matthew":[21],"effects.":[22],"Here":[23],"we":[24],"propose":[25],"LLM-Metrics,":[26],"a":[27,179,205,218,238],"research-impact":[28],"assessment":[29],"derived":[31],"parametric":[34,71],"memory":[35,72,195],"of":[36,73,80,122,136,178,226],"large":[37],"language":[38],"models":[39,67,228],"(LLMs).":[40],"The":[41],"central":[42],"hypothesis":[43,220],"is":[44],"that":[45,55,66],"high-impact":[46],"papers":[47,98],"receive":[48],"greater":[49],"exposure":[50,57],"in":[51,62,100,221],"academic":[53],"community,":[54],"this":[56],"enters":[58],"LLM":[59],"training":[60],"data":[61],"textual":[63],"form,":[64],"consequently":[68],"form":[69],"stronger":[70,159],"these":[74],"papers.":[75],"We":[76],"designed":[77],"four":[78],"types":[79],"multiple-choice":[81],"probes,":[82],"covering":[83],"title":[84],"recognition,":[85,87,89,92],"author":[86],"method":[88],"venue":[91],"evaluated":[94],"549":[95],"computer":[96],"science":[97],"published":[99],"2023-2024":[101],"across":[102],"17":[103,115],"LLMs":[104],"spanning":[105],"0.5B":[106],"to":[107],"72B":[108],"parameters":[109],"six":[111],"vendors.":[112],"Of":[113],"models,":[116,216],"15":[117],"produced":[118],"positive":[119],"predictions,":[120],"9":[121],"which":[123,222],"were":[124,169,203],"significant":[125],"at":[126,172],"p":[127,141],"less":[128],"than":[129],"0.05,":[130],"with":[131,192,209],"an":[132,193,232],"overall":[133],"Spearman":[134],"correlation":[135],"rho":[137,163,210],"=":[138,142,164,211],"0.1495":[139],"0.0004":[143],"against":[144],"citation":[145,167],"counts.":[146],"Three":[147],"additional":[148],"findings":[149],"support":[150],"proposed":[152],"mechanism.":[153,196],"First,":[154],"predictive":[156,201],"signal":[157],"was":[158],"2024":[161],"papers,":[162],"0.1880,":[165],"whose":[166],"near":[170],"zero":[171],"model-training":[173],"time,":[174],"reducing":[175],"plausibility":[177],"simple":[180],"reverse-causality":[181],"explanation.":[182],"Second,":[183],"author-recognition":[184],"probes":[185],"showed":[186],"strongest":[188],"discriminative":[189],"power,":[190],"consistent":[191],"exposure-driven":[194],"Third,":[197],"model":[198],"scale":[199],"power":[202],"non-monotonic:":[204],"3B-parameter":[206],"model,":[207],"Llama-3.2-3B-Instruct,":[208],"0.1829,":[212],"outperformed":[213],"most":[214],"larger":[215],"supporting":[217],"selective-memory":[219],"limited":[224],"capacity":[225],"smaller":[227],"can":[229],"serve":[230],"as":[231],"effective":[233],"information":[234],"filter.":[235],"LLM-Metrics":[236],"offers":[237],"real-time,":[239],"cross-disciplinary,":[240],"citation-independent":[241],"paradigm":[242],"assessment.":[245]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-23T00:00:00"}
