{"id":"https://openalex.org/W4404608864","doi":"https://doi.org/10.1109/icmlca63499.2024.10753879","title":"Leveraging Large Language Models: Enhancing Retrieval-Augmented Generation with ScaNN and Gemma for Superior AI Response","display_name":"Leveraging Large Language Models: Enhancing Retrieval-Augmented Generation with ScaNN and Gemma for Superior AI Response","publication_year":2024,"publication_date":"2024-10-18","ids":{"openalex":"https://openalex.org/W4404608864","doi":"https://doi.org/10.1109/icmlca63499.2024.10753879"},"language":"en","primary_location":{"id":"doi:10.1109/icmlca63499.2024.10753879","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icmlca63499.2024.10753879","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 5th International Conference on Machine Learning and Computer Application (ICMLCA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5069107358","display_name":"Min Gao","orcid":"https://orcid.org/0009-0005-2109-897X"},"institutions":[{"id":"https://openalex.org/I165075387","display_name":"Trine University","ror":"https://ror.org/038e0dv78","country_code":"US","type":"education","lineage":["https://openalex.org/I165075387"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Min Gao","raw_affiliation_strings":["Trine University,Allen Park,USA"],"affiliations":[{"raw_affiliation_string":"Trine University,Allen Park,USA","institution_ids":["https://openalex.org/I165075387"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102993243","display_name":"Ping Lu","orcid":"https://orcid.org/0000-0002-8864-9810"},"institutions":[{"id":"https://openalex.org/I111088046","display_name":"Boston University","ror":"https://ror.org/05qwgg493","country_code":"US","type":"education","lineage":["https://openalex.org/I111088046"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peiqing Lu","raw_affiliation_strings":["Boston universit,New Jersey,USA"],"affiliations":[{"raw_affiliation_string":"Boston universit,New Jersey,USA","institution_ids":["https://openalex.org/I111088046"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045922557","display_name":"Zihao Zhao","orcid":"https://orcid.org/0000-0003-2486-4535"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zihao Zhao","raw_affiliation_strings":["Stevens Institute of Technology,Hoboken,USA"],"affiliations":[{"raw_affiliation_string":"Stevens Institute of Technology,Hoboken,USA","institution_ids":["https://openalex.org/I108468826"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081174767","display_name":"Xiaowei Bi","orcid":null},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaowei Bi","raw_affiliation_strings":["Northwestern University,Chicago,USA"],"affiliations":[{"raw_affiliation_string":"Northwestern University,Chicago,USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101437735","display_name":"Fan Wang","orcid":"https://orcid.org/0000-0003-3542-6960"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fa Wang","raw_affiliation_strings":["Meta,Newark,USA"],"affiliations":[{"raw_affiliation_string":"Meta,Newark,USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5069107358"],"corresponding_institution_ids":["https://openalex.org/I165075387"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18355209,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"619","last_page":"622"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9950000047683716,"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":0.9950000047683716,"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":0.9745000004768372,"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/gemma","display_name":"Gemma","score":0.900328516960144},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7378536462783813},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5331810116767883},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.45971205830574036},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.42892634868621826},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.335691899061203},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.10023611783981323}],"concepts":[{"id":"https://openalex.org/C170806853","wikidata":"https://www.wikidata.org/wiki/Q2746099","display_name":"Gemma","level":2,"score":0.900328516960144},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7378536462783813},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5331810116767883},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.45971205830574036},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.42892634868621826},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.335691899061203},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.10023611783981323},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmlca63499.2024.10753879","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icmlca63499.2024.10753879","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 5th International Conference on Machine Learning and Computer Application (ICMLCA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2912817604","https://openalex.org/W3021397474","https://openalex.org/W3099700870","https://openalex.org/W3172119680","https://openalex.org/W3209791570","https://openalex.org/W4402962595","https://openalex.org/W6777399232","https://openalex.org/W6777615688","https://openalex.org/W6778883912","https://openalex.org/W6779872132","https://openalex.org/W6779985061","https://openalex.org/W6784189300","https://openalex.org/W6793840336"],"related_works":["https://openalex.org/W4310415663","https://openalex.org/W2087549296","https://openalex.org/W4322733123","https://openalex.org/W4252484528","https://openalex.org/W4241616324","https://openalex.org/W2046569361","https://openalex.org/W2385613214","https://openalex.org/W1998849792","https://openalex.org/W1924124684","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Retrieval-Augmented":[0],"Generation":[1],"(RAG)":[2],"systems":[3,25],"are":[4],"increasingly":[5],"vital":[6],"in":[7,29,87,115],"developing":[8],"AI":[9,138],"assistants":[10],"capable":[11],"of":[12,37],"answering":[13],"complex":[14],"queries":[15],"by":[16,71,83],"combining":[17,125],"retrieval":[18,30,67,127],"and":[19,33,69,96,113,128,136,149],"generation":[20,82],"capabilities.":[21],"However,":[22],"existing":[23],"RAG":[24,45],"often":[26],"face":[27],"limitations":[28],"efficiency,":[31],"scalability,":[32],"the":[34,49,57,76],"contextual":[35],"relevance":[36],"generated":[38],"responses.":[39,116],"This":[40],"study":[41],"presents":[42],"an":[43],"enhanced":[44],"system":[46,119],"that":[47],"integrates":[48],"Scalable":[50],"Nearest":[51],"Neighbors":[52],"(ScaNN)":[53],"search":[54],"algorithm":[55],"with":[56],"Gemma":[58,77],"language":[59,78,129],"model":[60,79],"to":[61],"address":[62],"these":[63],"challenges.":[64],"ScaNN":[65],"improves":[66],"speed":[68],"accuracy":[70],"leveraging":[72],"anisotropic":[73],"hashing,":[74],"while":[75],"enhances":[80],"response":[81],"processing":[84],"retrieved":[85],"information":[86],"context.":[88],"Through":[89],"meticulous":[90],"architectural":[91],"design,":[92],"advanced":[93],"data":[94],"preprocessing,":[95],"comprehensive":[97],"evaluation":[98],"metrics,":[99],"our":[100],"approach":[101],"demonstrates":[102],"significant":[103],"performance":[104],"gains":[105],"over":[106],"baseline":[107],"models,":[108],"achieving":[109],"improved":[110],"accuracy,":[111],"relevance,":[112],"coherence":[114],"The":[117],"proposed":[118],"establishes":[120],"a":[121,133],"novel":[122],"direction":[123],"for":[124,140],"state-of-the-art":[126],"modeling":[130],"techniques,":[131],"offering":[132],"more":[134],"efficient":[135],"effective":[137],"solution":[139],"diverse":[141],"applications":[142],"such":[143],"as":[144],"customer":[145],"service,":[146],"academic":[147],"research,":[148],"professional":[150],"decision-making.":[151]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}