{"id":"https://openalex.org/W7157008356","doi":"https://doi.org/10.48550/arxiv.2604.22985","title":"Uncertainty Quantification for LLM Function-Calling","display_name":"Uncertainty Quantification for LLM Function-Calling","publication_year":2026,"publication_date":"2026-04-24","ids":{"openalex":"https://openalex.org/W7157008356","doi":"https://doi.org/10.48550/arxiv.2604.22985"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.22985","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22985","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.2604.22985","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029199865","display_name":"Zihuiwen Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Zihuiwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092441982","display_name":"Lukas Aichberger","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aichberger, Lukas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134770674","display_name":"Michael Kirchhof","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kirchhof, Michael","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130325089","display_name":"Sinead Williamson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Williamson, Sinead","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043365442","display_name":"Luca Zappella","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zappella, Luca","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029186201","display_name":"Yarin Gal","orcid":"https://orcid.org/0000-0002-2733-2078"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gal, Yarin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086300347","display_name":"Arno Blaas","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Blaas, Arno","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134818590","display_name":"Adam Golinski","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Golinski, Adam","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":0.2696000039577484,"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":0.2696000039577484,"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":0.17170000076293945,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.07090000063180923,"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/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.6503999829292297},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5633000135421753},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5281999707221985},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.483599990606308},{"id":"https://openalex.org/keywords/syntax","display_name":"Syntax","score":0.4661000072956085},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4099999964237213}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7465000152587891},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.6503999829292297},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5633000135421753},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5281999707221985},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.483599990606308},{"id":"https://openalex.org/C60048249","wikidata":"https://www.wikidata.org/wiki/Q37437","display_name":"Syntax","level":2,"score":0.4661000072956085},{"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/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4099999964237213},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3912999927997589},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.3813000023365021},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.3587000072002411},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3343000113964081},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C114408938","wikidata":"https://www.wikidata.org/wiki/Q333373","display_name":"Abstract syntax","level":3,"score":0.2587999999523163},{"id":"https://openalex.org/C2986422732","wikidata":"https://www.wikidata.org/wiki/Q753025","display_name":"Auto tuning","level":4,"score":0.25699999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.22985","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22985","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.2604.22985","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22985","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":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"are":[4,46],"increasingly":[5],"deployed":[6],"to":[7,60,74,84,101,159],"autonomously":[8],"solve":[9],"real-world":[10],"tasks.":[11],"A":[12],"key":[13],"ingredient":[14],"for":[15,26,110,125],"this":[16,86,95,168],"is":[17,56],"the":[18,62,70,104,134,151,161],"LLM":[19,34,111],"Function-Calling":[20,112],"paradigm,":[21],"a":[22,66],"widely":[23],"used":[24,83],"approach":[25],"equipping":[27],"LLMs":[28],"with":[29],"tool-use":[30],"capabilities.":[31],"However,":[32],"an":[33],"calling":[35],"functions":[36],"incorrectly":[37],"can":[38,81,156,190],"have":[39],"severe":[40],"implications,":[41],"especially":[42],"when":[43,199],"their":[44,181],"effects":[45],"irreversible,":[47],"e.g.,":[48],"transferring":[49],"money":[50],"or":[51],"deleting":[52],"data.":[53],"Hence,":[54],"it":[55,137],"of":[57,107,153,163],"paramount":[58],"importance":[59],"consider":[61],"LLM's":[63],"confidence":[64,87],"that":[65,132,150],"function":[67,92],"call":[68],"solves":[69],"task":[71],"correctly":[72],"prior":[73],"executing":[75],"it.":[76],"Uncertainty":[77],"Quantification":[78],"(UQ)":[79],"methods":[80,109,166,173,189],"be":[82,157,191],"quantify":[85],"and":[88],"prevent":[89],"potentially":[90],"incorrect":[91],"calls.":[93],"In":[94],"work,":[96],"we":[97,130,148],"present":[98],"what":[99],"is,":[100],"our":[102],"knowledge,":[103],"first":[105],"evaluation":[106],"UQ":[108,116,145,165,172,188],"(FC).":[113],"While":[114],"multi-sample":[115,171],"methods,":[117],"such":[118],"as":[119],"Semantic":[120],"Entropy,":[121],"show":[122],"strong":[123],"performance":[124,162],"natural":[126],"language":[127],"Q&amp;A":[128],"tasks,":[129],"find":[131,149],"in":[133,167],"FC":[135,154,177],"setting,":[136],"offers":[138],"no":[139],"clear":[140],"advantage":[141],"over":[142],"simple":[143],"single-sample":[144,187],"methods.":[146],"Additionally,":[147],"particularities":[152],"outputs":[155,178],"leveraged":[158],"improve":[160],"existing":[164],"setting.":[169],"Specifically,":[170],"benefit":[174],"from":[175],"clustering":[176],"based":[179],"on":[180],"abstract":[182],"syntax":[183],"tree":[184],"parsing,":[185],"while":[186],"improved":[192],"by":[193],"selecting":[194],"only":[195],"semantically":[196],"meaningful":[197],"tokens":[198],"calculating":[200],"logit-based":[201],"uncertainty":[202],"scores.":[203]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-29T00:00:00"}
