{"id":"https://openalex.org/W7208524746","doi":null,"title":"Enhancing Search Systems with Large Language Models","display_name":"Enhancing Search Systems with Large Language Models","publication_year":2026,"publication_date":"2026-04-01","ids":{"openalex":"https://openalex.org/W7208524746"},"language":"en","primary_location":{"id":"pmh:oai:scholarcommons.scu.edu:eng_phd_theses-1065","doi":null,"is_oa":true,"landing_page_url":"https://scholarcommons.scu.edu/eng_phd_theses/66","pdf_url":"https://scholarcommons.scu.edu/cgi/viewcontent.cgi?article=1065&context=eng_phd_theses","source":{"id":"https://openalex.org/S4377196452","display_name":"Scholar Commons (Santa Clara University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I16269868","host_organization_name":"Santa Clara University","host_organization_lineage":["https://openalex.org/I16269868"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Engineering Ph.D. Theses","raw_type":"text"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://scholarcommons.scu.edu/cgi/viewcontent.cgi?article=1065&context=eng_phd_theses","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Peng, Zhiyuan","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I16269868","display_name":"Santa Clara University","ror":"https://ror.org/03ypqe447","country_code":"US","type":"education","lineage":["https://openalex.org/I16269868"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Peng, Zhiyuan","raw_affiliation_strings":["Department of Computer Science and Engineering Santa Clara University Santa Clara , California 2026"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering Santa Clara University Santa Clara , California 2026","institution_ids":["https://openalex.org/I16269868"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I16269868"],"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9955999851226807,"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":0.9955999851226807,"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":0.003800000064074993,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.0003000000142492354,"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/large-language-models","display_name":"large language models","score":0.7459999918937683}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8399999737739563},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6859999895095825},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.656499981880188},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5849999785423279},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5692999958992004},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5436999797821045},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.462799996137619},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.44760000705718994},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44029998779296875},{"id":"https://openalex.org/C99016210","wikidata":"https://www.wikidata.org/wiki/Q5488129","display_name":"Query expansion","level":2,"score":0.399399995803833},{"id":"https://openalex.org/C192028432","wikidata":"https://www.wikidata.org/wiki/Q845739","display_name":"Query language","level":2,"score":0.3950999975204468},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.38679999113082886},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.36880001425743103},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.3540000021457672},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.34689998626708984},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33640000224113464},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.3273000121116638},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.3059999942779541},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C96956885","wikidata":"https://www.wikidata.org/wiki/Q6138701","display_name":"RDF query language","level":5,"score":0.2840999960899353},{"id":"https://openalex.org/C97854310","wikidata":"https://www.wikidata.org/wiki/Q19541","display_name":"Search engine","level":2,"score":0.2759999930858612},{"id":"https://openalex.org/C157692150","wikidata":"https://www.wikidata.org/wiki/Q2919848","display_name":"Query optimization","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C164120249","wikidata":"https://www.wikidata.org/wiki/Q995982","display_name":"Web search query","level":3,"score":0.2614000141620636},{"id":"https://openalex.org/C161156560","wikidata":"https://www.wikidata.org/wiki/Q1638872","display_name":"Document retrieval","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C2778956030","wikidata":"https://www.wikidata.org/wiki/Q5142477","display_name":"Cold start (automotive)","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2533000111579895},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.2531999945640564}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:scholarcommons.scu.edu:eng_phd_theses-1065","doi":null,"is_oa":true,"landing_page_url":"https://scholarcommons.scu.edu/eng_phd_theses/66","pdf_url":"https://scholarcommons.scu.edu/cgi/viewcontent.cgi?article=1065&context=eng_phd_theses","source":{"id":"https://openalex.org/S4377196452","display_name":"Scholar Commons (Santa Clara University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I16269868","host_organization_name":"Santa Clara University","host_organization_lineage":["https://openalex.org/I16269868"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Engineering Ph.D. Theses","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:scholarcommons.scu.edu:eng_phd_theses-1065","doi":null,"is_oa":true,"landing_page_url":"https://scholarcommons.scu.edu/eng_phd_theses/66","pdf_url":"https://scholarcommons.scu.edu/cgi/viewcontent.cgi?article=1065&context=eng_phd_theses","source":{"id":"https://openalex.org/S4377196452","display_name":"Scholar Commons (Santa Clara University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I16269868","host_organization_name":"Santa Clara University","host_organization_lineage":["https://openalex.org/I16269868"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Engineering Ph.D. Theses","raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7208524746.pdf","grobid_xml":"https://content.openalex.org/works/W7208524746.grobid-xml"},"referenced_works_count":84,"referenced_works":["https://openalex.org/W1661745278","https://openalex.org/W2093390569","https://openalex.org/W2581624817","https://openalex.org/W2620544484","https://openalex.org/W2946659172","https://openalex.org/W2963323070","https://openalex.org/W2970641574","https://openalex.org/W3001279689","https://openalex.org/W3002104146","https://openalex.org/W3007672467","https://openalex.org/W3015883388","https://openalex.org/W3015921265","https://openalex.org/W3027879771","https://openalex.org/W3038572442","https://openalex.org/W3081168214","https://openalex.org/W3085177480","https://openalex.org/W3096808751","https://openalex.org/W3100107515","https://openalex.org/W3100292568","https://openalex.org/W3130740619","https://openalex.org/W3153833878","https://openalex.org/W3155114168","https://openalex.org/W3168875417","https://openalex.org/W3173727191","https://openalex.org/W3180230246","https://openalex.org/W3198002980","https://openalex.org/W3206455169","https://openalex.org/W4213243595","https://openalex.org/W4221141694","https://openalex.org/W4221161695","https://openalex.org/W4224902962","https://openalex.org/W4226518707","https://openalex.org/W4285078071","https://openalex.org/W4287269874","https://openalex.org/W4287887521","https://openalex.org/W4297162632","https://openalex.org/W4304700915","https://openalex.org/W4322718191","https://openalex.org/W4368755400","https://openalex.org/W4384655724","https://openalex.org/W4385565410","https://openalex.org/W4385569733","https://openalex.org/W4385569963","https://openalex.org/W4385573057","https://openalex.org/W4385768107","https://openalex.org/W4386576685","https://openalex.org/W4387156646","https://openalex.org/W4387156782","https://openalex.org/W4389520055","https://openalex.org/W4389520508","https://openalex.org/W4389523765","https://openalex.org/W4390041933","https://openalex.org/W4390873881","https://openalex.org/W4391272947","https://openalex.org/W4392576636","https://openalex.org/W4400482034","https://openalex.org/W4400527956","https://openalex.org/W4400530959","https://openalex.org/W4401042808","https://openalex.org/W4401043313","https://openalex.org/W4401306886","https://openalex.org/W4402684150","https://openalex.org/W4404783279","https://openalex.org/W4405901329","https://openalex.org/W4409643680","https://openalex.org/W4410089370","https://openalex.org/W4410357164","https://openalex.org/W4410636400","https://openalex.org/W4411113192","https://openalex.org/W4411120380","https://openalex.org/W4414939355","https://openalex.org/W4415257590","https://openalex.org/W4415795708","https://openalex.org/W4416011514","https://openalex.org/W4416036933","https://openalex.org/W4416895257","https://openalex.org/W4417170712","https://openalex.org/W6892107230","https://openalex.org/W6947947872","https://openalex.org/W7077519595","https://openalex.org/W7092283700","https://openalex.org/W7131789684","https://openalex.org/W7133239970","https://openalex.org/W7166853358"],"related_works":["https://openalex.org/W4417065331","https://openalex.org/W4391114255","https://openalex.org/W4404351246","https://openalex.org/W4416036130","https://openalex.org/W7171720252","https://openalex.org/W4412877037","https://openalex.org/W7202290415","https://openalex.org/W4412886896","https://openalex.org/W4403675643","https://openalex.org/W6891861597"],"abstract_inverted_index":{"Search":[0,60],"systems":[1,38,319],"traditionally":[2],"balance":[3],"efficiency":[4,83],"and":[5,19,55,77,82,129,136,143,178,206,220,228,245,272,276,298,354,360,366,379,392,398,402,412],"effectiveness":[6],"by":[7],"decomposing":[8],"the":[9,74,217,327,386,407],"retrieval":[10,154],"process":[11],"into":[12,269],"a":[13,252,265,291,352],"pipeline":[14],"of":[15,28,73,199,293,331],"query":[16,95,112,390],"understanding,":[17],"retrieval,":[18],"reranking":[20,257,397,401],"to":[21,43,47,87,133,180,184,280,395],"surface":[22],"relevant":[23,338],"documents.":[24,190],"The":[25,191],"rapid":[26],"progress":[27,411],"large":[29],"language":[30],"models":[31],"(LLMs)":[32],"is":[33,371],"reshaping":[34],"this":[35,374],"paradigm:":[36],"modern":[37],"increasingly":[39],"aim":[40],"not":[41,341],"only":[42],"retrieve":[44],"information":[45],"but":[46,339],"produce":[48],"direct":[49],"answers,":[50],"while":[51,115],"remaining":[52],"reliable,":[53],"efficient,":[54],"scalable.":[56],"This":[57,125,262],"dissertation,":[58],"Enhancing":[59],"Systems":[61],"with":[62,157,233],"Large":[63],"Language":[64],"Models,":[65],"studies":[66],"how":[67,78],"LLMs":[68,183],"can":[69],"improve":[70],"each":[71],"stage":[72],"search":[75,90,318,384,417],"stack":[76],"new":[79],"evaluation":[80],"resources":[81,380],"measures":[84],"are":[85,172],"needed":[86],"deploy":[88],"LLM-powered":[89,259],"in":[91,97,140,155,167,258,415],"practice.":[92],"First,":[93],"for":[94,152,165,188,212,255,357,364],"understanding":[96,113,391],"large-scale":[98],"e-commerce":[99],"search,":[100],"we":[101,160,215,249,325],"propose":[102,221],"an":[103,234,282],"entityaware":[104],"multi-task":[105],"learning":[106,279,394],"framework":[107,356],"that":[108,238,285,308,381],"jointly":[109],"optimizes":[110,287],"multiple":[111],"tasks":[114],"incorporating":[116],"entities":[117],"mined":[118],"from":[119,174,389],"engagement":[120],"data":[121],"as":[122,317],"contextual":[123],"signals.":[124],"approach":[126],"reduces":[127],"development":[128],"maintenance":[130],"overhead":[131],"compared":[132],"task-specific":[134,169],"models,":[135],"yields":[137,302],"substantial":[138],"gains":[139,305],"both":[141],"offline":[142],"online":[144],"evaluations":[145],"on":[146,290],"metrics":[147],"at":[148],"industrial":[149],"scale.":[150],"Second,":[151],"dense":[153,201],"domains":[156],"limited":[158],"supervision,":[159],"introduce":[161],"soft":[162,170],"prompt":[163],"tuning":[164],"augmentation,":[166],"which":[168],"prompts":[171],"learned":[173],"small":[175],"labeled":[176],"sets":[177,297],"used":[179],"guide":[181],"open-source":[182],"generate":[185],"weak":[186],"queries":[187,229],"unlabeled":[189],"resulting":[192],"synthetic":[193],"query\u2013document":[194],"pairs":[195],"enable":[196],"effective":[197],"training":[198],"domain-adapted":[200],"retrievers,":[202],"outperforming":[203],"unsupervised":[204],"baselines":[205,307],"prior":[207],"LLM-based":[208,213,283],"augmentation":[209],"strategies.":[210],"Third,":[211],"reranking,":[214],"investigate":[216],"efficiency\u2013effectiveness":[218],"trade-off":[219],"hardware-agnostic":[222],"metrics,":[223,378],"ranking":[224],"quality":[225],"per":[226,230],"PetaFLOP":[227],"PetaFLOP,":[231],"together":[232],"interpretable":[235],"FLOPs":[236],"estimator":[237],"enables":[239],"principled":[240],"comparisons":[241],"across":[242,385],"model":[243],"sizes":[244],"inference":[246],"configurations.":[247],"Fourth,":[248],"develop":[250],"MemRerank,":[251],"preference-memory":[253],"module":[254,263],"product":[256],"shopping":[260],"assistants.":[261],"distills":[264],"user\u2019s":[266],"purchase":[267,294],"history":[268,311],"structured":[270],"within-category":[271],"cross-category":[273],"preference":[274],"signals":[275],"uses":[277],"reinforcement":[278],"train":[281],"extractor":[284],"directly":[286],"setwise":[288],"reranking;":[289],"benchmark":[292],"histories,":[295],"candidate":[296],"relevance":[299],"labels,":[300],"MemRerank":[301],"significant":[303],"personalization":[304],"over":[306],"inject":[309],"raw":[310],"or":[312],"employ":[313],"untrained":[314],"extractors.":[315],"Finally,":[316],"evolve":[320],"toward":[321],"retrieval-augmented":[322],"question":[323,404],"answering,":[324,405],"study":[326],"critical":[328],"failure":[329],"mode":[330],"out-of-scope":[332,358],"questions,":[333,359],"where":[334],"retrieved":[335],"passages":[336],"appear":[337],"do":[340],"contain":[342],"sufficient":[343],"evidence,":[344],"often":[345],"triggering":[346],"hallucinated":[347],"answers.":[348],"We":[349],"present":[350],"ELOQ,":[351],"dataset":[353],"generation":[355],"demonstrate":[361],"improved":[362],"methods":[363],"detecting":[365],"responding":[367],"appropriately":[368],"when":[369],"evidence":[370],"insufficient.":[372],"Collectively,":[373],"dissertation":[375],"provides":[376],"methods,":[377],"advance":[382],"LLM-enhanced":[383],"full":[387],"pipeline,":[388],"representation":[393],"efficient":[396],"personalised":[399],"memory-based":[400],"reliable":[403],"bridging":[406],"gap":[408],"between":[409],"research":[410],"practical":[413],"deployment":[414],"real-world":[416],"systems.":[418]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-09-04T00:00:00"}