大語言模型驅(qū)動的AI新時代中電信運營商的思考與實踐

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Contents

Acknowledgement 3

Abstract 4

Preface 4

KT'sAITransformationutilizingAgentandData 4

NTTDOCOMO'sStrategicJourneytowardsDigitalTransformationandEnhanced

CustomerExperience 5

ChinaMobile'sTransitiontoAI+toAmplifyScaleEmpowerment 5

1LLMAdoptionStrategiesinIndustry 6

2EmergingChallengesandTechnicalForesights 7

2.1AIApplicationPerspective 7

2.2DataFuelingPerspective 9

3ApplicationToolingPlatforms 11

3.1ChinaMobileJiutianLargeLanguageModelApplicationPlatform 11

3.2DOCOMOLLMValue-AddedPlatform 12

3.3KTSLM/LLMPlatform 13

4GenerativeAIApplicationCases 14

4.1GenerativeAIforNetworkO&M 14

4.2GenerativeAIforCustomerService 17

5FutureOutlookandIndustrySuggestions 21

6Abbreviations 22

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Acknowledgement

SCFAwasestablishedin2011byChinaMobile,Korea'sKT,andJapan'sNTTDOCOMO,aimingtopromoteatripartitecooperationframeworkforglobaltechnologystandardsandindustryecosystems.

In2022,theAIWorkgroupwasestablished,focusingonthedevelopmentandapplicationofAItechnology,promotingtechnicalexchangesamongmembercompanies,andguidingandfacilitatingtheapplicationandcooperationofAItechnologywithintheindustry.

ThisWhitePaperhasbeenproducedasacollectiveeffortwithintheSCFAAIWG,andonitsbehalfthefollowingeditingteam(listedinalphabeticalorder):

ChinaMobile:

LingliDeng,BoYuan,XuefengZhao,XiangyangYuan,DiJin

KT:

JiyoungKim,JaehoOh

NTTDOCOMO:

IsseiNakamura,KuanyinLiu,AoguYamada,SatomiKura,TakeshiKato

SCFAAIWG

ChinaMobileContact:

liukaixi@

KTContact:

zeeyoung.kim@

NTTDOCOMOContact:

issei.nakamura.zs@

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Abstract

ThisdocumentanalyzesthechallengesofscaleadoptionofLargeLanguageModels(LLMs)intoindustrialapplications,highlightingtheproblemofreinventingthewheelofcommoncapabilities,theperformancebottleneckofnetworkcommunication,theimprovementofproductivitybyutilizingwork-orientedSLM/LLMbasedAIagents,andproposestechnologicaldevelopmenttrendssuchasinnovationinfundamentalalgorithms,standardizationofapplicationtoolplatforms,andCloud-Edgecollaboration.ItshowcasescontributingCSPs’strategiclayoutinAItechnology,dataintegration,applicationtoolingplatforms,aswellasavarietyofgenerativeAIapplications,andlooksforwardtothefuturedevelopmentofAItechnology,dataintegrationandindustrycollaborationrecommendations.

Preface

KT'sAITransformationutilizingAgentandData

WiththerapidadvancementofAIHWandSWtechnologies,generativeAImodelsareevolvingintovariousversions.Alongsidethis,generativeAIAgentsareswiftlypermeatingourdailylives.TheparadigmshiftstoapracticalAIAgentcompetition,reflectingusers'GenAIdemands,iscloselyrelatedtothehandlingandaccommodationofextensivecustomerdata.AsAIadvances,theimportanceofdataincorporateactivitieshasbecomeevengreater,andData-drivenAIAgentsbasedoncustomersandcompaniesareatthecenterof"CorporateTransformationUsingAI".TosucceedinAX,itisessentialtocollectandutilizedatafromcorporateactivitieseffectively,andtheprimaryinnovationofAIcompaniesmustbedrivenbyData-drivenAX.

Inthe"EraofAIAgents",whereAIisbecomingcentraltocorporateandpersonaldailyservices,KTispursuingtheenhancementofAIcompetitivenessusingAIAgentsasoneofitssuccessfultransformationdirectionsintoanAICTcompany.Underthemulti-modelline-upstrategy,whichcombinesitsself-developedAIlanguagemodelMi:dmwithmodelsbasedonopen-source,KTaimstoprovideavarietyofcustomer/industry-specificmodelsandAIAgentstothemarket,basedonhigh-qualitydatalearningandutilization.KTismovingforwardwiththegoalofenhancingproductivitybyutilizingworkAIAgentsforitsemployees,anditalsoplanstospreadnewAIexperiencestocustomersbyapplyingthemtoitsGenieTV.BydevelopingtheseAIAgentsandlaunchingservices,KTexpectstosecurecustomerAIdataandconceivespecificAIbusinessmodelsutilizingthedata.StrengtheningAIMSPcompetitivenessbyprovidingModelasaServicecomprehensivelyandthroughglobalAIAgenttechnology/businesscooperation,KTwillleadtheAImarketandecosystemconstruction.

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NTTDOCOMO'sStrategicJourneytowardsDigitalTransformationandEnhancedCustomerExperience

NTTDOCOMO(DOCOMO)setthegoalofimprovingcustomerexperienceandreformingbusinessstructurewithdigitalizationofbusinessmanagement,andpromotionandexecutionofdatautilizationasourmedium-termstrategytoward2025.InitiativesindigitaltransformationatDOCOMOincludenetworkoptimizationthroughdatautilization,AIandhumanresourcetraining,andthepromotionofdigitalmarketing.AIplatformsforimagerecognition,voicerecognition,andcustomeranalysisarebeingofferedtoenhanceDOCOMO'scompetitivenessbyapplyingthesetechnologiestoitsservices.

Since2014,DOCOMOhasbeenbuildingabigdatainfrastructurethatcollectsdatasuchasuserinformation,usagehistory,networktrafficandpaymenthistoryfromalmost100millionusersandmorethan270,000basestationsasanefforttopromotedigitalizationofbusinessmanagementanddatautilization.TheplatformincorporatesexternaldatafrombusinesspartnersandAItechnologiestocreatevalueacrossvariousbusinessfields,suchasMobilityasaService,retail,banking,andthemetaverse.

LeveragingnewtechnologieslikegenerativeAItofindnewrevenuestreamsandgrowthebusinessisnotaneasytask.Itrequiresstrategicplanning,includingtrainingpersonnel,andalotoftrialanderror.DOCOMOisnotonlyfocusingondevelopingthefoundationaltechnologiesforgenerativeAIbutisalsoactivelyworkingonvariousinitiativestocreateusecasesandtrainpersonnelthroughcontinuousexperimentationandrefinement.

ChinaMobile'sTransitiontoAI+toAmplifyScaleEmpowerment

Inthefaceofthewaveofchange,ChinaMobile,asthelargestmobilecommunicationoperatorintheworld,hasalwaysanchoreditsstrategicpositioningof"world-classinformationservicetechnologyinnovationcompany".

Intermsofnetworkcomputinginfrastructure,acommunicationnetworkwiththewidestcoverageandthelargestuserscaleintheworldhasbeenbuilt,withmorethan1.9million5Gbasestationsaccountingfor30%oftheworld'stotal,over90landandseacablesystemsconnecting78countries,andthelargestsingleintelligentcomputingcenterofglobaloperatorswith18000GPUcards.

Jiutian,aseriesoflargefoundationmodelsoflanguage,vision,voice,structureddataandmulti-modalityhavebeenconstructed,ontopofwhichmorethan40largeindustrymodelsarelaunched,formingacomprehensiveAIportfolioincludingplatforms,capabilities,andlarge-scaleapplications.Over10,000"AI+"projectshavebeenlaunchedtopromotetheintelligentandgreendevelopmentofvariousindustries,suchasenergy,manufacturing,medicalcaring,transportationandothers.

Alongtheway,itisnoticedthatthetransitionto"AI+"signifiestheshiftofAItechnologyfromameretechnicalapplicationtoacomprehensiveempowermentdeeplyintegratedintoindustrialdevelopment.Thechallengesfacedinthisprocessincludethe

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limitationsofLLMsincriticaltaskexecution,thewasteofresourcescausedbytherepetitivedevelopmentofcommoncapabilities,andthebottleneckeffectofnetworkcommunication.

Toaddressthesechallenges,ChinaMobilecallsonallpartiesintheindustrytoworktogetherinbuildingacomprehensive"AI+"industryecosystemtopromoteinnovationsatthefundamentalalgorithmlevel,standardizationofapplicationtoolingplatforms,andnewmodelsofCloud-Edgecollaboration

1LLMAdoptionStrategiesinIndustry

Artificialintelligence,representingthenewgenerationofinformationtechnology,israpidlyemergingasasignificantdrivingforcefornewqualityproductivity.Amongthese,generativeAItechnologybasedonLLMsissignificantlyempoweringvariousindustries,leadingtoanexplosivegrowthintheapplicationofAImodelsacrossindustries,heraldingthearrivalofatechnologicalandindustrialrevolution,wheretheinformationservicesystemandtheeconomicandsocialoperationsystemsaredeeplyintegrated,profoundlychangingpeople'slifestylesandmodesofproduction.

LLMshavedemonstratedextensiveandprofoundimpactsoncurrentindustrialapplications,emergingaspivotaltoolsinthedigitaltransformationofenterprises.Fromknowledgemanagementtohandlingcomplextasks,LLMsareprogressivelyintegratingintocorebusinessprocesses.Onenotableapplicationisretrieval-augmentedgeneration(RAG),whichcombinesexternalknowledgebaseswithgenerativecapabilitiestoeffectivelyaddresscomplexqueries.Thisapproachisparticularlyeffectiveincustomerservice,whereLLMsassistcompaniesinextractingpreciseanswersfrommassiveinternaldocuments,therebyenhancingserviceefficiency.Moreover,LLMsplayasignificantroleinbuildingandmanagingenterpriseknowledgebases,facilitatingintelligentqueryingandupdatingthroughnaturallanguageunderstandingandknowledgeextraction.Inhandlingcomplextasks,LLMsexhibitpowerfulcapabilitiessuchasautomatedreportwriting,marketingcopygeneration,andcodegeneration,significantlyboostingproductivityandautomatingbusinessprocesses.LLMshavealsofoundwidespreaduseinautomatedcustomerservicesystems,wheretheirdeepunderstandingofnaturallanguageallowsthemtohandlecomplexcustomerintentionsandcontextualinteractionsbeyondthereachoftraditionalchatbots.Additionally,LLMscontributetopersonalizedrecommendationsbygeneratingcustomizedcontent,offeringprecisesuggestionsthathelpbusinessesachievehighercustomersatisfaction.Torealizetheseapplications,LLMsleveragevarioustechniquestooptimizetheirperformanceinspecificscenarios.TheadoptionofLLMsinindustrycanproceedindifferentways,dependingonthetechnologicalrequirementsandapplicationcontext.Forapplicationswithlowertechnicalbarriers,enterprisescanquicklydeployL0andL1modelsbyintegratingdomain-specificknowledgebases,makingthisapproachsuitableforscenariosthatrequirerapidimplementationwithoutintensivemodeloptimization.Inscenariosrequiringdomain-specificcustomization,L0modelscanbefine-tunedbyuploadingcustomizeddatasetsandapplyinglow-codeconfigurationtoproduceL1modelsadaptedtospecifictasks.Thismethodsuitssituationswheredata

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accumulationandmodeladaptabilityareneeded,allowingformorepreciseresponsestoparticularbusinessrequirements.Forapplicationswithhighertechnicaldemandsandmorecomplexcontexts,enterprisescanadoptacomprehensivemodeldevelopmentprocess,encompassingdatacollection,processing,pre-training,andfine-tuning,ensuringmodelperformanceandstabilityinintricateapplicationsandmeetingtheneedsofhigh-precision,high-reliabilityoperations.Furthermore,LLMdeploymentcanberealizedthroughmulti-modelconvergenceplatforms,enablingbroadercollaborativeapplications.Enterprisescanutilizemodularpluginsandcentralizedagentstobuildcomplexbusinesssystemsthatintegratemultiplemodels,therebyfacilitatingcross-industryapplicationexpansionandfulfillingtherequirementsofsophisticatedapplicationecosystems.

Inconclusion,theindustrialdeploymentofLLMsspansfrombasicknowledgebaseintegrationtofull-scalemodelcustomizationandmulti-modelmanagement,creatingamulti-layeredapplicationsystemthatrangesfromlowtechnicalbarrierstohighlycustomizedimplementations.Throughthesediverseapproaches,LLMsaredrivingthedevelopmentofintelligentindustries,providingflexibleandpersonalizedsolutionsacrosssectors,andempoweringenterpriseswithefficientoperationsandintelligentdecision-makingcapabilities.

2EmergingChallengesandTechnicalForesights

Withthein-depthdevelopmentofthefourthindustrialrevolutioncharacterizedbydigitalintelligence,thereisaforeseeabletrendofthemutualembracebetweentraditionalindustriesandAItechnologytoaddressemergingchallengesforLLMscaleadoption:ontheonehand,thedeepeningintegrationofindustryinformationresourcesanddatagovernanceempowerstheinnovationofLLMapplicationsbyprovidingdesiredrawdatamaterials;ontheotherhand,continuousinnovationinLLMalgorithmsandengineeringtoolsaddressestheapplicabilityandeconomicissuesoflarge-scaleproductionenvironmentapplications.

2.1AIApplicationPerspective

Challenge:Largelanguagemodelscurrentlydonotpossessthecapabilitytobedirectly

appliedinkeydecision-makingprocessesinproductionenvironments.

Foresight:Innovationinbasictheoriesforreasoningacceleration,full-processautonomouscontrolatthefundamentalalgorithmlevel,torealizeautonomouscognition,autonomousevolution,andautonomousbreakthroughofAIagents.

Currently,LLMsserveaspowerfulinformationprocessingtoolscapableofexecutingtaskssuchasnaturallanguageprocessing,imagerecognition,languagetranslation,textgeneration,andimagerecognition.However,largelanguagemodelsthemselveslackenvironmentalperceptioncapabilitiesanddonotpossessautonomyandproactivedecision-makingabilities,usuallyrequiringhumaninputortriggeringtoprocess

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informationinapresetmanner.Therefore,theyfacedifficultiesinexecutingdynamicandcomplextasks,asthesetaskstypicallyrequireperceptionandunderstandingoftherealworld,theabilitytoadapttoenvironmentalchanges,andmakingdecisionsthatalignwiththegoals.Hencefutureinnovationatthebasicalgorithmlevelwillfocusonthefollowingareas:

lAutonomouscognitionFuturealgorithmswillplacegreateremphasisontheautonomouscognitivecapabilitiesofintelligentagents,enablingthemtobetterunderstandandpredicttheirenvironment,withenhancedperception,reasoning,anddecision-makingcapabilitiesoftheenvironment,aswellasadaptabilityincomplexenvironments.

lAutonomousevolutionAlgorithmswillbedesignedtoevolveontheirown,continuouslyoptimizingtheirperformancethroughmachinelearning.Intelligentagentswillbeabletolearnfromexperience,automaticallyadjusttheirbehaviortoadapttonewtasksandenvironments,therebyimprovingtheirgeneralizationcapabilities.

lAutonomousbreakthroughToachieveahigherlevelofintelligence,algorithmsneedtobeabletoachievebreakthroughsontheirownwithouthumanintervention.Thisinvolvesinnovativealgorithmdesign,enablingAIagentstodiscovernewsolutionsandevensurpasstheperformanceofhumanexpertsinsomecases.

Moreover,tosupportthedevelopmentoftheabovecapabilities,algorithmsandAIagentoperationoptimizationandcontroltechnologyalsoneediterativeinnovation,includingreasoningaccelerationtechnologytoimprovetheresponsivenessandefficiencyofAIagentsforcomplextasks,andfull-processautonomouscontrollablealgorithmstoensuretheirstabilityandreliability.

Challenge:Theverticalrepetitivedevelopmentofalargenumberofcommon

capabilitiesleadstoresourcewasteandslowsupdatesandupgrades.

Foresight:TheriseofapplicationtoolingplatformsservingasLLMsplusdomainspecificknowledgebases,withplugins,tools,enhancingprofessionalcapabilitieswhilenotlosingbasiccapabilitiesforAIagentcustomizationdevelopment.

Inthecurrentfieldofartificialintelligence,wefaceasignificantchallenge,thatis,theverticalrepetitivedevelopmentofalargenumberofcommoncapabilities,whichnotonlyleadstoresourcewastebutalsomakestheprocessofupdatesandupgradesslow.ThisphenomenonisparticularlyprominentintherapidlydevelopingAItechnologybecauseitinvolvesalargeamountofresearchandapplicationdevelopment.

Toaddressthischallenge,itisforeseenthatanimportantdirectionforfuturetechnologicaldevelopmentistheinnovationofapplicationtoolplatforms.Inparticular,AIagentcustomizationanddevelopmentplatformswillbekey,whichcanprovidelow-codesolutionstoenablenon-technicaluserstocreateofficeagents,financialagents,andotherprofessionaltoolseasily.SuchplatformsprovidebasicLLMscombinedwithprofessionalknowledgebases,aswellaspluginsandtools,whichcanenhanceprofessionalcapabilitieswhilekeepingbasiccapabilities.

Throughsuchplatforms,onemaynotonlyreduceresourcewastebutalsoacceleratetheadvancementofAItechnology,therebypromotingthehealthydevelopmentofthe

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entireindustry.

Challenge:The"bottleneckeffect"ofnetworkinconnectingdataandcloudcomputing

infrastructureishighlightedasthe"lastmile"ofLLMdeploymentanduserempowerment.

Foresight:Cloud-Edgecollaborationisleveragedtoenablepremise(networkedge,hometerminal)personalizedAIagentservices.

Intoday'sdigitalera,thebottleneckeffectofnetworkcommunicationhasbecometherestricting"lastmile"forLLMstoreachandempowerusers.Tosolvethisproblem,itisforeseeablethatthenewmodelofCloud-Edgecollaborationwillbecomemainstream,especiallyontheend-sideofthenetworkedgeandhometerminal,byprovidingpersonalizedintelligentagentservicesasasolution.

Thenetworkedgeandhometerminalontheend-sidearekeylinksintheCloud-Edgecollaboration,andAIagentservicescanbedeployedattheseendpointstoreducethedependenceoncentralizedcloudcomputingresources.Inthisway,datapre-processing,analysis,andresponsecanbeexecutedclosertotheuser,reducingdatatransmissionlatencyandbandwidthrequirements.e.g.,bydeployingintelligentgatewaysathometerminals,functionslikehomeautomationcontrolandsecuritymonitoringcanberealizedwithimprovedresponsivenessandreducednetworkload.

Inaddition,basedontheAIagentcustomizationanddevelopmentplatform,personalizedAIagentservicescanbecustomizedaccordingtothespecificneedsandusagehabitsofusers,providingmoreaccurateandefficientservices.Thisnotonlyincludesapplicationsinprofessionalfieldssuchasofficeagentsandfinancialagentsbutcanalsobeextendedtovariousaspectsoflifesuchaspersonalhealthmanagement,education,andentertainment.BycallingontheLLMsandprofessionalknowledgebasesdistributedintheend-to-endnetworkondemand,integratingpluginsandtools,etc.,personalizedAIagentscanenhancetheirprofessionalcapabilitieswhilenotlosingresponsivenessorcustomerexperience.

Insummary,throughthedevelopmentofCloud-EdgecollaborationandpersonalizedAIagentservices,thebottleneckproblemofnetworkcommunicationcanbeeffectivelysolved,promotingthewidespreadapplicationofLLMsinvariousfieldsandachievingatrueintelligenttransformation.

2.2DataFuelingPerspective

Challenge:Thelackofstandardizationofscattereddatahindersthestartingpointfor

data-drivenAX.

Foresight:DataGovernancefordataclassification,datastandardizationandsystematization,andgrademanagementofdata.

DatagovernanceisaseriesofprocessesrelatedtodatastandardizationforAI,toensureconsistencyindatanames,datadescriptions,anddataformats.

Thefollowingthreestagesarenecessarytoimplementdatagovernancesuccessfully.Meaningfulclassificationofcompany-widedataItiscrucialtosystematically

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classifyvarioustypesofcompany-widedata,suchasenterprisedata,customerdata,managementdata,andinfrastructuredata,accordingtotheirtypesandpurposes.Systematicclassificationofdataisthestartingpointforefficientmanagement,utilization,andexecutionofAXinthenearfuture.

StandardizationandsystematizationofclassifieddataItisnecessarytomanageandunifystandardssothatcustomerscanunderstandfromthesameperspectiveatanycontactpointwiththepossibilityofconnectionsbetweencompany-widedata.Additionally,toimprovethereadabilityofbusinessdatabyapplyingdatastandardizationandsecureAIutilizationisneeded.

Managingdatagradesandconstructinggrade-basedcloudsconnectedwiththeappropriatesecuritysystemsItisessentialtoestablishagradingsystembycreatingmanagementindicators(quality,utilization,andcost)fordataandaccordinglyconfiguringgrade-basedclouds.Fromthesecurityenhancementperspective,itshouldbeavailabletochooseaccesscontrol,monitoring,andlogmanagementaccordingtothedatagrade.

Challenge:Dataintegrationisrequiredtomanagedatathatmakesunfragmentedinoneplace.

Foresight:Cloud-basedintegratedplatformfordatacentralization,analysis,andmodeling.

Itisrequiredtobuildacloud-basedMLdataplatformthatcancentralizecompany-widedatatoresolveexistingdataissues.

Buildinganintegrateddataplatformhelpscentralizethedataandgraduallyresolvetheissuescausedbydatasilos.

Tocontinuouslymanagethedataintegrationeffectively,itisnecessarytoconsistentlyalignamodernizationofAI,Data,andITinfrastructuresothattheprocessofdataaccumulationbythealignmentbetweenAIandDataandavailabilityofassetsbythealignmentbetweenDataandITcontinuestocirculate.

Throughthedirectionofdatacollectionandavailabilityofassets,itisexpectedtoachievetheeffectssuchasimprovingdecision-making,andpredictingissuesbyutilizingcustomerdata,managementdata,andinfrastructuredata.

Challenge:DataServingshouldbepreparedtointegrateanddistributethedataappropriately.

Foresight:Company-widecollaboration,secureandaccumulationofcapabilities,datamonetization.

Eveniftheprocessofintegrateddatagovernanceandmanagementiscarriedoutproperly,itcannotbesaidthatdata-drivenAXhasbeenfullyrealized.

Toeffectivelyintegratetheaccumulateddataanddistributeitasneeded,adedicatedorganizationthatleadsdataplanningandexecutionmustbeestablishedaswellasacollaborativesystembasedondomain-specificMLOps.

Anexpertiseindatagovernanceanddomain-specificdatacanbesecuredthroughsuchacollaborativesystem.

Additionally,itisnecessarytoexpanddatautilizationbusinessesbasedontheacquired

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dataoperationandmanagementcapabilitiesandtoconvertthisexperienceintoexternalbusinesscapabilities.

3ApplicationToolingPlatforms

Inresponsetonumerouschallengesthatgreatlylimittheefficiencyofusersinbuildingintelligentagentsduringthedevelopmentprocess,suchashightechnicalbarriers,longdevelopmentcycles,difficultiesinimprovingmodelperformance,complexdeploymentandmaintenance,insufficientcustomizationandflexibility,difficultiesinteamcollaboration,andensuringsecuritycompliance,bothChinaMobile'sJiutianLargeLanguageModelApplicationPlatformandDOCOMO'sLLMValue-AddedPlatformenableone-stopintelligentagentapplicationdevelopment.

3.1ChinaMobileJiutianLargeLanguageModelApplicationPlatform

ChinaMobile'sJiutianLargeLanguageModelApplicationPlatformhascapabilitiessuchasapplicationconstruction,pluginintegration,modelplayground,andinferenceservices,offeringafull-process,one-stopproductiontoolforLLMapplications.Itprovidesacombinationofautonomousplanningandschedulingwithcontrollablemanualschedulingtoimproveschedulingaccuracyandreducemodelhallucinations,achievesenhancedmanagementofprivatedomainknowledgebasestoimprovetheaccuracyandprofessionalismofanswers,integratesarichsetofofficialpluginstofacilitatetheconstructionofabroaderrangeofapplicationcapabilities,integratesvariousmemorycapabilitiestopersonalizemodelresponsesandintegrateswiththird-partyapplicationstoprovideaccesstoAPIsandotherinferenceservices,whichhelpsindividualandenterprisecustomerstodeveloptheirownAIapplicationsatalowcostandinatimelyfashion,promotingtheapplicationandimplementationofLLMsinvariousindustries.

Figure1IllustrativeWorkflowofJiutianLargeLanguageModelApplicationPlatform

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AsshowninFigure1,theJiutianLargeLanguageModelApplicationPlatformprovidesone-stopintelligentagentservicesforindividualandenterprisecustomers,insupportingmorethan100,000userstoquicklybuildmorethan1,500customizedintelligentagentapplications,coveringmultiplescenariossuchasoffice,social,entertainment,anddailylife,helpingAItoempowervariousindustries.

Lookingtothefuture,consumers'needsarebecomingincreasinglycomplex,andhigherrequirementswillbeproposedforthequality,stability,andrefinementofservices.Toempoweruserstobuilddiverseandcomplexapplications,theplatformwillfocusonstandardizingprocesses,supportingmultimodaldata,low-codeworkflows,andoptimizingthecorecapabilitiesofintelligentagents.Bycomprehensivelyupgradingintelligentagentservices,itensuresexcellentquality,stability,andreliability,enrichesthepluginecosystem,andprovidesanefficient,intelligent,andcomprehensiveconstructionexperience,inordertohelpitscustomersseizetheinitiativeindigitaltransformation,acceleratethepaceofinnovation,andachievealeapinbusinessvalue.

3.2DOCOMOLLMValue-AddedPlatform

SinceAugust2023,DOCOMOhavebeendevelopingtheLLMValue-AddedPlatformtopromotedigitaltransformationwithinourinternaloperationsandprovidenewservicesusingLLMs.ThisplatformisutilizedwithintheDOCOMOGroup,boastingapproximately7,000monthlyactiveusersandaround1,000,000callspermonth.

Themajorfeaturesavailableontheplatforminclude:

lLLMTherearevariousLLMsavailableasopen-sourcesoftware(OSS)orsoftwareasaservice(SaaS).TheseLLMsdifferintermsofcost,inp