{"id":"https://openalex.org/W4409955391","doi":"https://doi.org/10.32604/cmc.2025.063721","title":"An Analytical Review of Large Language Models Leveraging KDGI Fine-Tuning, Quantum Embedding\u2019s, and Multimodal Architectures","display_name":"An Analytical Review of Large Language Models Leveraging KDGI Fine-Tuning, Quantum Embedding\u2019s, and Multimodal Architectures","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4409955391","doi":"https://doi.org/10.32604/cmc.2025.063721"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.063721","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.063721","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2025.063721","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073586335","display_name":"Uddagiri Sirisha","orcid":"https://orcid.org/0000-0003-2998-3402"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Uddagiri Sirisha","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016344045","display_name":"Chanumolu Kiran Kumar","orcid":"https://orcid.org/0000-0002-2537-8718"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chanumolu Kiran Kumar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058586078","display_name":"Revathi Durgam","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Revathi Durgam","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021164142","display_name":"Poluru Eswaraiah","orcid":"https://orcid.org/0000-0002-8799-2641"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Poluru Eswaraiah","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5077328343","display_name":"G. Muni Nagamani","orcid":"https://orcid.org/0000-0002-6534-4950"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"G Muni Nagamani","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5073586335"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.6849,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.96102413,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"83","issue":"3","first_page":"4031","last_page":"4059"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9560999870300293,"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":0.9560999870300293,"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/embedding","display_name":"Embedding","score":0.6824968457221985},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.624687135219574},{"id":"https://openalex.org/keywords/quantum","display_name":"Quantum","score":0.4784291386604309},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3185826539993286},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.15676471590995789},{"id":"https://openalex.org/keywords/quantum-mechanics","display_name":"Quantum mechanics","score":0.09616872668266296}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6824968457221985},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.624687135219574},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.4784291386604309},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3185826539993286},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.15676471590995789},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.09616872668266296}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2025.063721","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.063721","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2025.063721","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.063721","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W4206017443","https://openalex.org/W4285192560","https://openalex.org/W4376122030","https://openalex.org/W4382498938","https://openalex.org/W4386558807","https://openalex.org/W4387821331","https://openalex.org/W4387937021","https://openalex.org/W4389165057","https://openalex.org/W4389991792","https://openalex.org/W4390590960","https://openalex.org/W4391407054","https://openalex.org/W4391561379","https://openalex.org/W4392114301","https://openalex.org/W4392646953","https://openalex.org/W4392739577","https://openalex.org/W4393078503","https://openalex.org/W4393113494","https://openalex.org/W4393284236","https://openalex.org/W4394773586","https://openalex.org/W4394897028","https://openalex.org/W4395069539","https://openalex.org/W4395083633","https://openalex.org/W4396506226","https://openalex.org/W4396820575","https://openalex.org/W4396831355","https://openalex.org/W4396835373","https://openalex.org/W4396982300","https://openalex.org/W4398774961","https://openalex.org/W4399154371","https://openalex.org/W4399619108","https://openalex.org/W4399768519","https://openalex.org/W4399939183","https://openalex.org/W4400118952","https://openalex.org/W4400387039","https://openalex.org/W4400447474","https://openalex.org/W4401210496","https://openalex.org/W4401387006","https://openalex.org/W4401717597","https://openalex.org/W4401770219","https://openalex.org/W4401834466","https://openalex.org/W4401878776","https://openalex.org/W4401906673","https://openalex.org/W4402125078","https://openalex.org/W4402352041","https://openalex.org/W4402512774","https://openalex.org/W4402582321","https://openalex.org/W4402742693","https://openalex.org/W4402901320","https://openalex.org/W4403059180","https://openalex.org/W4403277434","https://openalex.org/W4403446803","https://openalex.org/W4403471894","https://openalex.org/W4403674737","https://openalex.org/W4403863524","https://openalex.org/W4403924970","https://openalex.org/W4404239715","https://openalex.org/W4404467068","https://openalex.org/W4404486407","https://openalex.org/W4404639422","https://openalex.org/W4405399726","https://openalex.org/W4405463625","https://openalex.org/W4405590246","https://openalex.org/W4405596328","https://openalex.org/W4405597099","https://openalex.org/W4405645597","https://openalex.org/W4405700344","https://openalex.org/W4406084794"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2081900870","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890"],"abstract_inverted_index":{"A":[0],"complete":[1],"examination":[2],"of":[3,32,52,253],"Large":[4],"Language":[5],"Models\u2019":[6],"strengths,":[7,36],"problems,":[8],"and":[9,27,37,49,56,73,79,100,115,126,136,149,168,201,222,225,231,238,242,256],"applications":[10,48],"is":[11],"needed":[12],"due":[13],"to":[14,228],"their":[15],"rising":[16],"use":[17],"across":[18],"disciplines.":[19],"Current":[20],"studies":[21,63],"frequently":[22],"focus":[23],"on":[24,64],"single-use":[25],"situations":[26],"lack":[28],"a":[29],"comprehensive":[30],"understanding":[31],"LLM":[33,54,86,258],"architectural":[34],"performance,":[35,203],"weaknesses.":[38],"This":[39,248],"gap":[40],"precludes":[41],"finding":[42],"the":[43,154,205,215,251],"appropriate":[44],"models":[45,117,209],"for":[46,121,130,173,207,245],"task-specific":[47],"limits":[50],"awareness":[51],"emerging":[53],"optimization":[55],"deployment":[57],"strategies.":[58],"In":[59,176],"this":[60],"research,":[61],"50":[62],"25+":[65],"LLMs,":[66],"including":[67],"GPT-3,":[68],"GPT-4,":[69],"Claude":[70],"3.5,":[71],"DeepKet,":[72],"hybrid":[74],"multimodal":[75,101,186,226],"frameworks":[76],"like":[77,105,139,195],"ContextDET":[78],"GeoRSCLIP,":[80,188],"are":[81,119,143,151,157],"thoroughly":[82],"reviewed.":[83],"We":[84],"propose":[85],"application":[87,259],"taxonomy":[88],"by":[89,92],"grouping":[90],"techniques":[91],"task":[93],"focus\u2014healthcare,":[94],"chemistry,":[95],"sentiment":[96],"analysis,":[97],"agent-based":[98],"simulations,":[99],"integration.":[102],"Advanced":[103],"methods":[104,150],"parameter-efficient":[106],"tuning":[107,193],"(LoRA),":[108],"quantum-enhanced":[109,169],"embeddings":[110,170],"(DeepKet),":[111],"retrieval-augmented":[112],"generation":[113],"(RAG),":[114],"safety-focused":[116],"(GalaxyGPT)":[118],"evaluated":[120],"dataset":[122],"requirements,":[123],"computational":[124],"efficiency,":[125],"performance":[127],"measures.":[128],"Frameworks":[129],"ethical":[131,257],"issues,":[132],"data":[133],"limited":[134],"hallucinations,":[135],"KDGI-enhanced":[137],"fine-tuning":[138],"Woodpecker\u2019s":[140],"post-remedy":[141],"corrections":[142],"highlighted.":[144],"The":[145,159,234],"investigation\u2019s":[146],"scope,":[147],"mad,":[148],"described,":[152],"but":[153],"primary":[155],"results":[156],"not.":[158],"work":[160,249],"reveals":[161],"that":[162],"domain-specialized":[163],"fine-tuned":[164],"LLMs":[165,227,244],"employing":[166],"RAG":[167],"perform":[171],"better":[172],"context-heavy":[174],"applications.":[175],"medical":[177],"text":[178],"normalization,":[179],"ChatGPT-4":[180],"outperforms":[181],"previous":[182],"models,":[183,218],"while":[184],"two":[185],"frameworks,":[187],"increase":[189],"remote":[190],"sensing.":[191],"Parameter-efficient":[192],"technologies":[194],"LoRA":[196],"have":[197],"minimal":[198],"computing":[199],"cost":[200],"similar":[202],"demonstrating":[204],"necessity":[206],"adaptive":[208],"in":[210],"multiple":[211],"domains.":[212],"To":[213],"discover":[214],"optimum":[216],"domain-specific":[217,220],"explain":[219],"fine-tuning,":[221],"present":[223],"quantum":[224],"address":[229],"scalability":[230],"cross-domain":[232],"issues.":[233],"framework":[235],"helps":[236],"academics":[237],"practitioners":[239],"identify,":[240],"adapt,":[241],"innovate":[243],"different":[246],"purposes.":[247],"advances":[250],"field":[252],"efficient,":[254],"interpretable,":[255],"research.":[260]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}