大型语言模型 LLM：2023 年完整指南

大型语言模型LLM：2023年完整指南LargeLanguageModels:CompleteGuidein2023n时间和金钱Savetimeandmoneyon训练Trainingss大型语言模型(LLM)是在自然语言处理(NLP)和自然语言生成(NLG)任务中利用深度学ngZeroonefewshotlearning这些模型可以适应下游(特定)任务(见图2)。目标是预测接下来可能出现的文本。模型的复杂程度和性能可以通过它有多大型语言模型示例种用例和行业，包括医疗保健、零售、科技等。以下是所有行业中存在的⚫聊天机器人、虚拟助手和对话式AIn语言模型图3)。大型语言模型的4大优势性化和客户满意度4-环境影响许多客户希望企业能够24/7全天候可用，这可以通过使用语言模型的聊天机器人和虚拟助手来实动化内容创建，语言模型可以通过处理大量数据来了解客户行为和偏好，从而推动个性系将随着可用性和个性化服务而增加。可以自动化营销、销售、人力资源和客户服务中的许多流程。例如，语言模型可以帮为企业节省时间的另一个领域是分析大量数据。凭借处理大量信息的能力，企业可以数据集中快速提取见解并做出明智的决策。这可以提高运营效率、更快地解决问题和更明的准确性理大量数据，从而提高预测和分类任务的准确性。模型使用此信息来学习模式它们做出更好的预测和分组。户评论是正面、负面还是中立的准确性。这种准确性的提高在许多业务应用程序中至关限性性和偏置力仅限于它们所训练的文本训练数据，这意味着它们对世界的了解有限。这些模型学限制为2048个代币(大约1500个单词)，这意味着ChatGPT无法理解输入并为超过2048个代3-系统成本开发大型语言模型需要计算机系统、人力资本(工程师、研究人员、科学家等)和电力方面的大量威震天-图灵由数百台NVIDIADGXA100多GPU服务器开发，每台服务器使用高达6.5千瓦的功UBERTgemodelsChatGPTsettherecordforthefastest-growinguserbaseinJanuary2023,provingthatlanguagemodelsareheretostay.answertoChatGPTwasintroducedinFebruaryealsoopeningnewpossibilitiesforbusinessesastheycaneyYet,largelanguagemodelsareanewdevelopmentincomputerscience.Becauseofthis,businessleadersmaynotbeup-to-dateonsuchmodels.WewrotethisarticletoinformcuriousbusinessleadersinlargelargelanguagemodelelanguagemodelsLLMsarefoundationmodelsthatutilizedeeplearninginnaturallanguageprocessing(NLP)andnaturallanguagegeneration(NLG)tasks.Forthepurposeofhelpingthemlearnthetechniquessuchasngthesemodelscanbeadaptedfordownstreamspecifictasks(seeFigure2).AnLLMisessentiallyaTransformer-basedneuralnetwork,introducedinanarticlebyGoogleengineersnext.Thesophisticationandperformanceofamodelcanbejudgedbyhowmanyparametersithas.AberoffactorsitconsiderswhengeneratingoutputherearemanyopensourcelanguagemodelsthataredeployableonpremiseorinaprivatecloudwhichomelargelanguagemodelsinthiscategoryMostoftheleadinglanguagemodeldevelopersareAmerican,buttherearesuccessfulexamplesfromaandEuropeastheyworktocatchupongenerativeAItheusecasesoflanguagemodelsLargelanguagemodelscanbeappliedtoavarietyofusecasesandindustries,includinghealthcare,retail,ThefollowingareusecasesthatexistinallindustriesnetrainedLargelanguagemodelsaredeeplearningneuralnetworks,asubsetofartificialintelligenceandmachineainedsothattheylearnbasiclanguagetasksandfunctionsquiresmassivecomputationalpowerandcuttingedgehardwaretrainingandfinetuningforlanguagemodelsingvsfinetuningSourcemediumcomOncethemodelispre-trained,itcanbetrainedwithtask-specificnewdatatofine-tuneitforspecificusecases.Thefine-tuningmethodhashighcomputationalefficiencysinceitrequireslessdataandpower,makingitacheapermethod(seeFigure3).checkourLargeLanguageModelTraininginarticleflargelanguagemodelscostsdtoautomatemanyprocessessuchasAutomatingsuchtasksleadstoreducedmanuallaborandrelatedcostsersatisfactionManycustomersexpectbusinessestobeavailable24/7,whichisachievablethroughchatbotsandvirtualassistantsthatutilizelanguagemodels.Withautomatedcontentcreation,languagemodelscandrivepersonalizationbyprocessinglargeamountsofdatatounderstandcustomerbehaviorandpreferences.onandpositivebrandrelationswillincreasewithavailabilityandpersonalizedservicetemanyprocessesinmarketingsalesHRandcustomerserviceForexample,languagemodelscanhelpwithdataentry,customerservice,anddocumentcreation,freeingupworkonmoreimportanttasksthatrequirehumanexpertiseAnotherareawherelanguagemodelscansavetimeforbusinessesisintheanalysisoflargeamountsofdataWiththeabilitytoprocessvastamountsofinformation,businessescanquicklyextractinsightsfromcomplexdatasetsandmakeinformeddecisions.Thiscanleadtoimprovedoperationalefficiency,fasterblemsolvingandbetterinformedbusinessdecisionsLargelanguagemodelsarecapableofprocessingvastamountsofdata,whichleadstoimprovedaccuracyinpredictionandclassificationtasks.Themodelsusethisinformationtolearnpatternsandrelationships,ichhelpsthemmakebetterpredictionsandgroupingsForexample,insentimentanalysis,alargelanguagemodelcananalyzethousandsofcustomerreviewstounderstandthesentimentbehindeachone,leadingtoimprovedaccuracyindeterminingwhetheraybusinesscationsassmallerrorscanhaveasignificantimpactationsoflanguagemodelsbiasLanguagemodels’capabilitiesarelimitedtothetextualtrainingdatatheyaretrainedwith,whichmeanstheyarelimitedintheirknowledgeoftheworld.Themodelslearntherelationshipswithinthetrainingandthesemayincludelshavebeenshowntomakeracistorsexistcomments.ealsoinstanceswheremodelscanpresentfalseinformationacertainnumberoftokensasinput.Forinstance,ChatGPThasalimitof2048tokens(around1,500words),whichmeansakesenseofinputsandgenerateoutputsforinputsexceedingthetokenlimitntensivemakesthedevelopmentoflargelanguagemodelsonlyavailabletohugeenterpriseswithvastresources.ItisestimatedthatMegatron-TuringfromNVIDIAandMicrosoft,hasatotalproject