NVIDIA - 英伟达 LLM day.pdf

1NVIDIA202301092DrivingtheFutureofEnterpriseWorkAIassistantswilldriveincreasedproductivityforeveryjobfunction•Intelligentchatbotsarethenextkillerenterpriseapplication•Humans"workwillchangefromhavingtodoalotofmanuallook-upsandgatheringofinformation,todirectingteamsofLLMsandpullingtogethertheresults•Enterpriseswillhave100-1000softheseAIassistantsintheircompanyacrosseveryjobfunction•ITspendisbeingincreasedtoadoptthesenewcopilotfeaturesbecausetheydriveincreaseproductivity,productdifferentiation,andimproveexperience•Thesechatbotswillhaveintelligenceaswellasaccesstoproprietaryinformation?LLMsArePowerfulToolsbutNotAccurateEnoughforEnterpriseWithoutaconnectiontoenterprisedatasources,LLMscannotprovideaccurateinformationUserFoundationModelPromptResponseRiskofoutdatedinformationHallucinationsLackingproprietaryknowledge5•Retrievalaugmentedgenerationintroduction•KeytechniquesinRAG•SolutionsfromNVIDIA•AIcopilotdemo–RAGcopilotAgenda•PatrickLewisetal.Retrieval-AugmentedGenerationforKnowledge-IntensiveNLPTasks[1]•General-purposefine-tuningrecipe•combinepre-trainedparametricandnon-parametricmemoryforlanguagegeneration•AtechniqueforenhancingtheaccuracyandreliabilityofgenerativeAImodelswithfactsfetchedfromexternalsources.•Thisapproachconstructsacomprehensivepromptenrichedwithcontext,historicaldata,andrecentorrelevantknowledge.WhatisRetrievalAugmentedGeneration(RAG)?RAGistoLLMswhatanopen-bookexamistohumans(1)Retrieve(2)Augment(3)Generate•GenerativeAIKnowledgeBaseChatbot|NVIDIA•Retrieval-AugmentedGeneration(RAG):FromTheorytoLangChainImplementation•Lewis,P.,etal.(2020).Retrieval-augmentedgenerationforknowledge-intensiveNLPtasks.AdvancesinNeuralInformationProcessingSystems,33,9459–9474.NextGenerationofEnterpriseApplicationsConnectLLMstoEnterpriseDataRetrievalAugmentedGenerationImprovesLLMPerformanceandEfficiencyImprovedAccuracyNaturalLanguageInterfaceContextualUnderstandingReducedComputationalCostsImprovedEfficiencyModelscananswerquestionsaboutinformationwithouthavingbeentrainedonthatdataHuman-readableoutputtextsthatareeasierforpeopletounderstand,raisingusertrustAImodelsbetterunderstandcontextwhengeneratingtextorotheroutputsReducedcomputationalcostsfromretrainingandmodelsizeatinferenceModelscanproducediverseoutputswithoutsacrificingaccuracyorefficiency$KeyTechniquesinRetrievalAugmentedGeneration(RAG)RAGistoLLMswhatanopen-bookexamistohumans(1)Retrieve(2)Augment(3)Generate•Non-parametricmemory(knowledgesource):•DocumentsLoader•EmbeddingModel•VectorDatabase•DatabaseSearch•Pre-trainedparametric(LLM)：•FoundationLLM•LLMDeploymentKeyTechniquesinRetrievalAugmentedGeneration(RAG)RAGistoLLMswhatanopen-bookexamistohumans(1)Retrieve(2)Augment(3)Generate•Non-parametricmemory(knowledgesource):•DocumentsLoader•VectorDatabase•EmbeddingModel•DatabaseSearch•Pre-trainedparametric(LLM)：•FoundationLLM•LLMDeployment10KeyTechniquesinRetrievalAugmentedGeneration(RAG)DocumentsLoader|VectorDatabase|EmbeddingModel|DatabaseSearchBuildEnterpriseRetrieval-AugmentedGenerationAppswithNVIDIARetrievalQA