{"id":"https://openalex.org/W4417456809","doi":"https://doi.org/10.48550/arxiv.2512.13237","title":"Learning to Retrieve with Weakened Labels: Robust Training under Label Noise","display_name":"Learning to Retrieve with Weakened Labels: Robust Training under Label Noise","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W4417456809","doi":"https://doi.org/10.48550/arxiv.2512.13237"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2512.13237","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.13237","pdf_url":"https://arxiv.org/pdf/2512.13237","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2512.13237","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102326178","display_name":"Arnab Sharma","orcid":"https://orcid.org/0009-0007-8515-5253"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Sharma, Arnab","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5102326178"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.5745999813079834,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.5745999813079834,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.14309999346733093,"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":0.07689999788999557,"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/noise","display_name":"Noise (video)","score":0.5730999708175659},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5541999936103821},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5378000140190125},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.52920001745224},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.42829999327659607},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.3903999924659729},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.3871000111103058},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.3677000105381012},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.36399999260902405}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7897999882698059},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5748999714851379},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5730999708175659},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5541999936103821},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5378000140190125},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.52920001745224},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4952000081539154},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.42829999327659607},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3903999924659729},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.3871000111103058},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.3677000105381012},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.36399999260902405},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.35339999198913574},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3474000096321106},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3449000120162964},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.34060001373291016},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3125},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.26339998841285706},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.25769999623298645},{"id":"https://openalex.org/C551230270","wikidata":"https://www.wikidata.org/wiki/Q4368942","display_name":"Data retrieval","level":2,"score":0.2529999911785126},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2500999867916107}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2512.13237","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.13237","pdf_url":"https://arxiv.org/pdf/2512.13237","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2512.13237","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.13237","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":"pmh:oai:arXiv.org:2512.13237","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.13237","pdf_url":"https://arxiv.org/pdf/2512.13237","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4417456809.pdf","grobid_xml":"https://content.openalex.org/works/W4417456809.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Neural":[0],"Encoders":[1],"are":[2],"frequently":[3],"used":[4],"in":[5,25,34,97,190],"the":[6,18,35,80,98,127,131,184,187],"NLP":[7],"domain":[8],"to":[9,16,41,53,71,79,92,169,192],"perform":[10,136],"dense":[11],"retrieval":[12,46,95,142,188],"tasks,":[13],"for":[14,21,111,118],"instance,":[15],"generate":[17,93,170],"candidate":[19],"documents":[20],"a":[22,88,106,119,158,164],"given":[23],"query":[24,113],"question-answering":[26],"tasks.":[27],"However,":[28],"sparse":[29],"annotation":[30],"and":[31,130],"label":[32,89,101,110,180],"noise":[33,166],"training":[36,74,81],"data":[37,63],"make":[38],"it":[39],"challenging":[40],"train":[42],"or":[43,62,75],"fine-tune":[44],"such":[45],"models.":[47],"Although":[48],"existing":[49],"works":[50],"have":[51],"attempted":[52],"mitigate":[54],"these":[55,65],"problems":[56],"by":[57,162],"incorporating":[58],"modified":[59],"loss":[60,196],"functions":[61],"cleaning,":[64],"approaches":[66],"either":[67],"require":[68],"some":[69],"hyperparameters":[70],"tune":[72],"during":[73],"add":[76],"substantial":[77],"complexity":[78],"setup.":[82],"In":[83],"this":[84,153],"work,":[85],"we":[86,116,155],"consider":[87,157],"weakening":[90,181],"approach":[91],"robust":[94],"models":[96],"presence":[99],"of":[100,104,121,173,186],"noise.":[102,174],"Instead":[103],"enforcing":[105],"single,":[107],"potentially":[108],"erroneous":[109],"each":[112],"document":[114],"pair,":[115],"allow":[117],"set":[120],"plausible":[122],"labels":[123],"derived":[124],"from":[125],"both":[126],"observed":[128],"supervision":[129],"model's":[132],"confidence":[133],"scores.":[134],"We":[135],"an":[137],"extensive":[138],"evaluation":[139],"considering":[140,147],"two":[141],"models,":[143],"one":[144],"re-ranking":[145],"model,":[146],"four":[148],"diverse":[149],"ranking":[150],"datasets.":[151],"To":[152],"end,":[154],"also":[156],"realistic":[159],"noisy":[160],"setting":[161],"using":[163],"semantic-aware":[165],"generation":[167],"technique":[168],"different":[171,194],"ratios":[172],"Our":[175],"initial":[176],"results":[177],"show":[178],"that":[179],"can":[182],"improve":[183],"performance":[185],"tasks":[189],"comparison":[191],"10":[193],"state-of-the-art":[195],"functions.":[197]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-12-17T00:00:00"}
