{"id":"https://openalex.org/W7160877619","doi":"https://doi.org/10.48550/arxiv.2605.07994","title":"Semantic Smoothing for Language Models via Distribution Estimation and Embeddings","display_name":"Semantic Smoothing for Language Models via Distribution Estimation and Embeddings","publication_year":2026,"publication_date":"2026-05-08","ids":{"openalex":"https://openalex.org/W7160877619","doi":"https://doi.org/10.48550/arxiv.2605.07994"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.07994","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07994","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.2605.07994","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123036234","display_name":"Haricharan Balasundaram","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Balasundaram, Haricharan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114412561","display_name":"Swathi Shree Narashiman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Narashiman, Swathi Shree","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135843995","display_name":"Pranay Mathur","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mathur, Pranay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135852834","display_name":"Andrew Thangaraj","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thangaraj, Andrew","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":0.6341000199317932,"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":0.6341000199317932,"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":0.1785999983549118,"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/T13629","display_name":"Text Readability and Simplification","score":0.031199999153614044,"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/smoothing","display_name":"Smoothing","score":0.8011000156402588},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6277999877929688},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5532000064849854},{"id":"https://openalex.org/keywords/perplexity","display_name":"Perplexity","score":0.5390999913215637},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4474000036716461},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.43939998745918274},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.3871999979019165},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.3488999903202057}],"concepts":[{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.8011000156402588},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6277999877929688},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5532000064849854},{"id":"https://openalex.org/C100279451","wikidata":"https://www.wikidata.org/wiki/Q372193","display_name":"Perplexity","level":3,"score":0.5390999913215637},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5113999843597412},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.45820000767707825},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4474000036716461},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.43939998745918274},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3944000005722046},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.3871999979019165},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.3488999903202057},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3400999903678894},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.32829999923706055},{"id":"https://openalex.org/C108757681","wikidata":"https://www.wikidata.org/wiki/Q2773912","display_name":"Bigram","level":3,"score":0.326200008392334},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.31790000200271606},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.3151000142097473},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.304500013589859},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.30379998683929443},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.07994","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07994","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.2605.07994","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07994","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,42,120],"propose":[1],"semantic":[2,73,148],"smoothing,":[3],"a":[4,25,33,47,89,93,112],"smoothing":[5,31,74,149],"method":[6],"for":[7,50,116],"language":[8,52],"models":[9,139],"that":[10,29,147],"uses":[11],"embeddings":[12,57,145],"to":[13,124,156],"share":[14],"statistical":[15],"observations":[16],"across":[17],"semantically":[18],"similar":[19],"contexts.":[20],"The":[21],"starting":[22],"point":[23],"is":[24],"decomposition":[26],"of":[27,35,55,60],"log-perplexity":[28],"motivates":[30],"as":[32,75],"collection":[34],"distribution-estimation":[36],"problems":[37],"under":[38,46],"Kullback-Leibler":[39],"(KL)":[40],"loss.":[41],"then":[43],"show":[44,146],"that,":[45],"Lipschitz-logit":[48],"model":[49],"embedding-based":[51],"generation,":[53],"proximity":[54,59],"context":[56],"implies":[58],"the":[61,122],"corresponding":[62],"next-word":[63],"distributions":[64],"in":[65,78],"KL":[66,79,97,107],"divergence.":[67],"Combining":[68],"these":[69],"observations,":[70],"we":[71,100],"formulate":[72],"distribution":[76,95],"estimation":[77],"loss":[80],"with":[81,92,105],"KL-proximity":[82],"side":[83,118],"information.":[84,119],"For":[85],"$n$":[86],"samples":[87],"on":[88,132],"$d$-symbol":[90],"alphabet":[91],"side-information":[94],"at":[96],"distance":[98],"$\u0394$,":[99],"give":[101],"an":[102],"interpolation":[103],"estimator":[104,123],"worst-case":[106],"risk":[108],"$O(\\min\\{\u0394,d/n\\})$,":[109],"and":[110,126,136,143,158],"prove":[111],"matching-order":[113],"lower":[114],"bound":[115],"uniform":[117],"extend":[121],"multiple":[125],"empirically":[127],"estimated":[128],"synonymous":[129],"distributions.":[130],"Experiments":[131],"synthetic":[133],"Markov":[134],"data":[135],"WikiText-103":[137],"bigram":[138],"using":[140],"Word2Vec,":[141],"GloVe,":[142],"GPT-2":[144],"consistently":[150],"reduces":[151],"test":[152],"perplexity":[153],"when":[154],"applied":[155],"add-constant":[157],"Kneser-Ney":[159],"estimates.":[160]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-12T00:00:00"}
