{"id":"https://openalex.org/W7163324208","doi":"https://doi.org/10.48550/arxiv.2606.02737","title":"Attention Calibration for Position-Fair Dense Information Retrieval","display_name":"Attention Calibration for Position-Fair Dense Information Retrieval","publication_year":2026,"publication_date":"2026-06-01","ids":{"openalex":"https://openalex.org/W7163324208","doi":"https://doi.org/10.48550/arxiv.2606.02737"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.02737","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.02737","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.2606.02737","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008587249","display_name":"Andrianos Michail","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michail, Andrianos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123879620","display_name":"Elias Schuhmacher","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schuhmacher, Elias","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137752820","display_name":"Juri Opitz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Opitz, Juri","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137731574","display_name":"Simon Clematide","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Clematide, Simon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137771122","display_name":"Rico Sennrich","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sennrich, Rico","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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.5192000269889832,"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"}},"topics":[{"id":"https://openalex.org/T10286","display_name":"Information Retrieval and Search Behavior","score":0.5192000269889832,"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/T10028","display_name":"Topic Modeling","score":0.14190000295639038,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.12880000472068787,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/calibration","display_name":"Calibration","score":0.640999972820282},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.520799994468689},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.46549999713897705},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.43959999084472656},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.37619999051094055},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.36010000109672546},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.3458000123500824},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3034999966621399}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6620000004768372},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.640999972820282},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.520799994468689},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.508899986743927},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.46549999713897705},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.43959999084472656},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.37619999051094055},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.36010000109672546},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35010001063346863},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.3458000123500824},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.30790001153945923},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3025999963283539},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.29660001397132874},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.2897999882698059},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2865000069141388},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2809999883174896},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C46900642","wikidata":"https://www.wikidata.org/wiki/Q2647","display_name":"Huffman coding","level":3,"score":0.27480000257492065},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C189391414","wikidata":"https://www.wikidata.org/wiki/Q7936579","display_name":"Visual Word","level":4,"score":0.2671999931335449}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.02737","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.02737","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.2606.02737","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.02737","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Dense":[0],"retrieval":[1,6,38,54,98],"models":[2,76,130],"exhibit":[3],"positional":[4,95,123],"bias:":[5],"effectiveness":[7],"degrades":[8],"when":[9],"relevant":[10],"information":[11],"appears":[12],"later":[13],"in":[14,162],"a":[15,59],"passage":[16],"(Zeng":[17],"et":[18,49],"al.,":[19,50],"2025).":[20],"We":[21,177],"ask":[22],"whether":[23],"this":[24,41],"bias":[25],"can":[26],"be":[27],"reduced":[28],"at":[29,182],"inference":[30],"time,":[31],"without":[32,35,131,146],"retraining":[33],"and":[34,55,68,79,89,97,134,139,154],"sacrificing":[36],"overall":[37],"effectiveness.":[39],"To":[40],"end,":[42],"we":[43,81],"adapt":[44],"inference-time":[45],"attention":[46,71],"calibration":[47,103],"(Schuhmacher":[48],"2026)":[51],"to":[52,136,148],"downstream":[53],"extend":[56],"it":[57],"with":[58],"strength":[60,90],"coefficient":[61],"lambda":[62],"that":[63,101],"interpolates":[64],"between":[65,94],"the":[66,92,117,158],"original":[67],"fully":[69],"calibrated":[70,86],"distributions.":[72],"Across":[73],"three":[74,129],"embedding":[75],"on":[77,125],"SQuAD-PosQ":[78],"FineWeb-PosQ,":[80],"examine":[82],"how":[83],"basket":[84],"size,":[85],"layer":[87,114],"set,":[88],"affect":[91],"trade-off":[93],"fairness":[96],"effectiveness,":[99],"finding":[100],"partial":[102],"frequently":[104],"outperforms":[105],"full":[106],"calibration.":[107],"A":[108],"single":[109],"configuration":[110,144],"(B=128,":[111],"lambda=0.5,":[112],"50%":[113],"depth)":[115],"improves":[116],"harmonic":[118],"mean":[119],"of":[120],"nDCG@10":[121],"across":[122],"groups":[124],"FineWeb-PosQ":[126],"for":[127],"all":[128,163],"per-model":[132],"tuning,":[133],"applies":[135],"both":[137],"-pooled":[138],"last-token-pooled":[140],"architectures.":[141],"This":[142],"default":[143],"transfers":[145],"modification":[147],"PosIR,":[149],"which":[150],"spans":[151],"10":[152],"languages":[153],"31":[155],"domains,":[156],"reducing":[157],"Position":[159],"Sensitivity":[160],"Index":[161],"16":[164],"length-quartile":[165],"x":[166,168],"model":[167],"retrieval-setting":[169],"combinations,":[170],"while":[171],"preserving":[172],"or":[173],"improving":[174],"aggregate":[175],"nDCG@10.":[176],"release":[178],"our":[179],"extended":[180],"codebase":[181],"https://github.com/impresso/fair-sentence-transformers":[183]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-04T00:00:00"}