{"id":"https://openalex.org/W7152961743","doi":"https://doi.org/10.48550/arxiv.2604.07816","title":"Tool Retrieval Bridge: Aligning Vague Instructions with Retriever Preferences via Bridge Model","display_name":"Tool Retrieval Bridge: Aligning Vague Instructions with Retriever Preferences via Bridge Model","publication_year":2026,"publication_date":"2026-04-09","ids":{"openalex":"https://openalex.org/W7152961743","doi":"https://doi.org/10.48550/arxiv.2604.07816"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.07816","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.07816","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":null,"license_id":null,"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.2604.07816","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133329640","display_name":"Kunfeng Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Kunfeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhuang, Luyao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhuang, Luyao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133348180","display_name":"Fei Liao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liao, Fei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111140222","display_name":"Juhua Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Juhua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133356465","display_name":"Jian Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133363344","display_name":"Bo Du","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Bo","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":0.4399999976158142,"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":0.4399999976158142,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.13179999589920044,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10203","display_name":"Recommender Systems and Techniques","score":0.03959999978542328,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/bridge","display_name":"Bridge (graph theory)","score":0.7318999767303467},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.621999979019165},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5541999936103821},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.49630001187324524},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4871000051498413},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.46399998664855957},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.4377000033855438}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8184999823570251},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.7318999767303467},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.621999979019165},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5824000239372253},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5541999936103821},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.49630001187324524},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4871000051498413},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4781999886035919},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.46399998664855957},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.4377000033855438},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.41690000891685486},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41119998693466187},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3953000009059906},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.38260000944137573},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3443000018596649},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3215000033378601},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.31779998540878296},{"id":"https://openalex.org/C59656382","wikidata":"https://www.wikidata.org/wiki/Q191536","display_name":"Conjunction (astronomy)","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.2700999975204468}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.07816","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.07816","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.07816","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.07816","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Tool":[0,119],"learning":[1],"has":[2],"emerged":[3],"as":[4],"a":[5,65,81,95,117,141,204],"promising":[6],"paradigm":[7],"for":[8,29,131],"large":[9],"language":[10],"models":[11,225],"(LLMs)":[12],"to":[13,18,85,124,139,144,209,219],"address":[14],"real-world":[15,59,73],"challenges.":[16],"Due":[17],"the":[19,31,69,107,126,146,155,173,180,198,213],"extensive":[20,164],"and":[21,56,100,153,160,172,188,224],"irregularly":[22],"updated":[23],"number":[24],"of":[25,97,109,128,136,182,200,207],"tools,":[26],"tool":[27,33,39,70,110,129],"retrieval":[28,40,71,130,170],"selecting":[30],"desired":[32],"subset":[34],"is":[35,138],"essential.":[36],"However,":[37],"current":[38],"methods":[41],"are":[42,61,226],"usually":[43],"based":[44],"on":[45,91],"academic":[46],"benchmarks":[47],"containing":[48],"overly":[49],"detailed":[50],"instructions":[51,60,104,148,159,184],"(e.g.,":[52],"specific":[53,151],"API":[54],"names":[55],"parameters),":[57],"while":[58,185],"more":[62,150],"vague.":[63],"Such":[64],"discrepancy":[66],"would":[67],"hinder":[68],"in":[72],"applications.":[74],"In":[75],"this":[76,113],"paper,":[77],"we":[78,93,115],"first":[79],"construct":[80],"new":[82],"benchmark,":[83],"VGToolBench,":[84],"simulate":[86],"human":[87],"vague":[88,103,132,147,158,183],"instructions.":[89,133],"Based":[90],"this,":[92],"conduct":[94,163],"series":[96],"preliminary":[98],"analyses":[99],"find":[101],"that":[102,176],"indeed":[105],"damage":[106],"performance":[108,127],"retrieval.":[111],"To":[112],"end,":[114],"propose":[116],"simple-yet-effective":[118],"Retrieval":[120],"Bridge":[121],"(TRB)":[122],"approach":[123],"boost":[125],"The":[134,221],"principle":[135],"TRB":[137,177],"introduce":[140],"bridge":[142],"model":[143],"rewrite":[145],"into":[149],"ones":[152],"alleviate":[154],"gap":[156],"between":[157],"retriever":[161],"preferences.We":[162],"experiments":[165],"under":[166],"multiple":[167],"commonly":[168],"used":[169],"settings,":[171],"results":[174],"show":[175],"effectively":[178],"mitigates":[179],"ambiguity":[181],"delivering":[186],"consistent":[187],"substantial":[189],"improvements":[190],"across":[191],"all":[192],"baseline":[193],"retrievers.":[194],"For":[195],"example,":[196],"with":[197],"help":[199],"TRB,":[201],"BM25":[202],"achieves":[203],"relative":[205],"improvement":[206],"up":[208],"111.51%,":[210],"i.e.,":[211],"increasing":[212],"average":[214],"NDCG":[215],"score":[216],"from":[217],"9.73":[218],"19.59.":[220],"source":[222],"code":[223],"publicly":[227],"available":[228],"at":[229],"https://github.com/kfchenhn/TRB.":[230]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-11T00:00:00"}