{"id":"https://openalex.org/W7167034539","doi":"https://doi.org/10.48550/arxiv.2607.00004","title":"Why Advanced Encoders Lag on Sparse Retrieval? The Answer and an Approach to Bridging Vocabulary Gaps","display_name":"Why Advanced Encoders Lag on Sparse Retrieval? The Answer and an Approach to Bridging Vocabulary Gaps","publication_year":2026,"publication_date":"2026-04-20","ids":{"openalex":"https://openalex.org/W7167034539","doi":"https://doi.org/10.48550/arxiv.2607.00004"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.00004","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.00004","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2607.00004","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139904077","display_name":"Zhichao Geng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Geng, Zhichao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139916080","display_name":"Yang Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Yang","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.2896000146865845,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.2896000146865845,"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":0.1551000028848648,"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"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.06610000133514404,"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/bridging","display_name":"Bridging (networking)","score":0.6913999915122986},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.6480000019073486},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6141999959945679},{"id":"https://openalex.org/keywords/intuition","display_name":"Intuition","score":0.3619000017642975},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.3427000045776367},{"id":"https://openalex.org/keywords/lossless-compression","display_name":"Lossless compression","score":0.3303000032901764},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.32580000162124634},{"id":"https://openalex.org/keywords/lag","display_name":"Lag","score":0.29580000042915344}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7303000092506409},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.6913999915122986},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.6480000019073486},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6141999959945679},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4925999939441681},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.47510001063346863},{"id":"https://openalex.org/C132010649","wikidata":"https://www.wikidata.org/wiki/Q189222","display_name":"Intuition","level":2,"score":0.3619000017642975},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.3427000045776367},{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.3303000032901764},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.32580000162124634},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.31610000133514404},{"id":"https://openalex.org/C75778745","wikidata":"https://www.wikidata.org/wiki/Q342626","display_name":"Lag","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.29280000925064087},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28630000352859497},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C2780310539","wikidata":"https://www.wikidata.org/wiki/Q12547192","display_name":"Imperfect","level":2,"score":0.2721000015735626},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2678999900817871},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C72169020","wikidata":"https://www.wikidata.org/wiki/Q194404","display_name":"Monotonic function","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C198942812","wikidata":"https://www.wikidata.org/wiki/Q496618","display_name":"Semantic property","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2628999948501587}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.00004","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.00004","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2607.00004","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.00004","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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/4","score":0.4997548460960388,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"While":[0],"advanced":[1,108],"foundation":[2],"models":[3,179],"like":[4,180],"ModernBERT":[5,163],"significantly":[6],"outperform":[7],"older":[8],"architectures":[9,187],"in":[10,21,153],"dense":[11,150],"retrieval,":[12],"they":[13],"surprisingly":[14],"lag":[15,197],"behind":[16],"the":[17,28,32,79,86,146,169,195],"aging":[18],"BERT-base":[19],"baseline":[20],"learned":[22],"sparse":[23],"retrieval":[24],"(LSR).":[25],"We":[26,64],"identify":[27],"root":[29],"cause":[30],"as":[31],"\\textit{Vocabulary":[33],"Gap}:":[34],"modern":[35],"tokenizers":[36],"utilize":[37],"raw,":[38],"case-sensitive":[39],"vocabularies":[40,113],"designed":[41],"for":[42],"lossless":[43],"reconstruction,":[44],"which":[45],"map":[46],"single":[47],"semantic":[48,91],"units":[49],"to":[50,110,127,138,164,185],"redundant":[51],"surface":[52],"forms,":[53],"wasting":[54],"model":[55],"capacity":[56],"on":[57,168],"morphological":[58],"noise":[59],"and":[60,131,149,182,188,212],"hindering":[61],"lexical":[62],"matching.":[63],"formalize":[65],"this":[66],"intuition":[67],"through":[68],"a":[69,103,120,174,204],"theoretical":[70],"framework,":[71],"demonstrating":[72],"that":[73,90,106,194],"appropriate":[74],"vocabulary":[75,206],"coarse-graining":[76],"can":[77],"tighten":[78],"generalization":[80],"bounds":[81],"by":[82],"reducing":[83],"complexity":[84],"of":[85],"hypothesis":[87],"class,":[88],"provided":[89],"integrity":[92],"is":[93,158,198],"preserved.":[94],"To":[95],"resolve":[96],"this,":[97],"we":[98],"propose":[99],"\\textbf{Vocabulary":[100],"Transfer":[101],"(VT)},":[102],"model-agnostic":[104],"framework":[105],"migrates":[107],"encoders":[109],"sparse-friendly,":[111],"normalized":[112],"with":[114,142],"minimal":[115],"computational":[116],"cost.":[117],"VT":[118,157],"utilizes":[119],"novel":[121],"\\textbf{Semantic":[122],"Initialization}":[123],"via":[124],"spatial":[125],"topology":[126],"preserve":[128],"geometric":[129],"structure":[130],"an":[132,200],"\\textbf{Activation":[133],"Potential":[134],"Calibration":[135],"(APC)}":[136],"mechanism":[137],"align":[139],"pre-trained":[140],"manifolds":[141],"sparsity":[143],"constraints,":[144],"preventing":[145],"dead":[147],"neuron":[148],"collapse":[151],"observed":[152],"standard":[154],"fine-tuning.":[155],"Empirically,":[156],"universally":[159],"effective:":[160],"it":[161],"enables":[162],"achieve":[165],"state-of-the-art":[166],"performance":[167,196],"BEIR":[170],"benchmark":[171],"(\\textbf{52.4}":[172],"nDCG,":[173],"\\textbf{+4.7}":[175],"improvement),":[176],"resuscitates":[177],"failing":[178],"RoBERTa-large,":[181],"generalizes":[183],"seamlessly":[184],"inference-free":[186],"specialized":[189],"domains.":[190],"These":[191],"results":[192],"confirm":[193],"not":[199],"architectural":[201],"deficiency":[202],"but":[203],"solvable":[205],"mismatch.":[207],"We've":[208],"released":[209],"our":[210],"code":[211],"models.\\footnote{https://anonymous.4open.science/r/vocab-transfer/.":[213],"All":[214],"details":[215],"included.}":[216]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-03T00:00:00"}