微软 -广义人工智能的火花GPT-4的早期实验 Sparks of Artificial General Intelligence：Early experiments with GPT-4

1SparksofArtificialGeneralIntelligence:EarlyexperimentswithGPT-4EricHorvitzEceKamarPeterLeeYinTatLeeYuanzhiLiScottLundbergHarshaNoriHamidPalangiMarcoTulioRibeiroYiZhangAbstractArtificialintelligence(AI)researchershavebeendevelopingandrefininglargelanguagemodels(LLMs)thatexhibitremarkablecapabilitiesacrossavarietyofdomainsandtasks,challengingourunderstandingoflearningandcognition.ThelatestmodeldevelopedbyOpenAI,GPT-4[Ope23],wastrainedusinganunprecedentedscaleofcomputeanddata.Inthispaper,wereportonourinvestigationofanearlyversionofGPT-4,whenitwasstillinactivedevelopmentbyOpenAI.Wecontendthat(thisearlyversionof)GPT-4ispartofanewcohortofLLMs(alongwithChatGPTandGoogle’sPaLMforexample)thatexhibitmoregeneralintelligencethanpreviousAImodels.Wediscusstherisingcapabilitiesandimplicationsofthesemodels.Wedemonstratethat,beyonditsmasteryoflanguage,GPT-4cansolvenovelanddifficulttasksthatspanmathematics,coding,vision,medicine,law,psychologyandmore,withoutneedinganyspecialprompting.Moreover,inallofthesetasks,GPT-4’sperformanceisstrikinglyclosetohuman-levelperformance,andoftenvastlysurpassespriormodelssuchasChatGPT.GiventhebreadthanddepthofGPT-4’scapabilities,webelievethatitcouldreasonablybeviewedasanearly(yetstillincomplete)versionofanartificialgeneralintelligence(AGI)system.InourexplorationofGPT-4,weputspecialemphasisondiscoveringitslimitations,andwediscussthechallengesaheadforadvancingtowardsdeeperandmorecomprehensiveversionsofAGI,includingthepossibleneedforpursuinganewparadigmthatmovesbeyondnext-wordprediction.Weconcludewithreflectionsonsocietalinfluencesoftherecenttechnologicalleapandfutureresearchdirections.Contents1Introduction41.1OurapproachtostudyingGPT-4’sintelligence 71.2Organizationofourdemonstration 82Multimodalandinterdisciplinarycomposition132.1Integrativeability 132.2Vision 162.2.1Imagegenerationbeyondmemorization 162.2.2Imagegenerationfollowingdetailedinstructions(`alaDall-E) 172.2.3Possibleapplicationinsketchgeneration 182.3Music 193Coding213.1Frominstructionstocode 213.1.1Codingchallenges 213.1.2Realworldscenarios 223.2Understandingexistingcode 2624Mathematicalabilities304.1AmathematicalconversationwithGPT-4 314.1.1Afirstgeneralizationoftheoriginalquestion 314.1.2Asecondvariantoftheoriginalquestion 324.1.3Analysisofthelimitationshighlightedbyconversation 344.2Performanceonmathematicalproblemdatasets 354.3Mathematicalmodelinginvariousdomains 374.4Higherlevelmathematics 395Interactionwiththeworld435.1Tooluse 435.1.1Usingmultipletoolstosolvemorecomplextasks 445.1.2Discussion 495.2EmbodiedInteraction 495.2.1Warmup:navigatingamap 495.2.2Text-basedgames 495.2.3Realworldproblems 525.2.4Discussion 536Interactionwithhumans546.1UnderstandingHumans:TheoryofMind 546.1.1Testingspecificaspectsoftheoryofmind 546.1.2Testingtheoryofmindinrealisticscenarios 546.1.3Discussion 606.2TalkingtoHumans:Explainability 607DiscriminativeCapabilities697.1PIIDetection 697.2MisconceptionsandFact-Checking 707.2.1WhyAreCurrentMetricsInsufficient? 717.2.2GPT-4asaJudge 738LimitationsofautoregressivearchitecturehighlightedbyGPT-4768.1Warm-upwithtwobasicexamples 768.2Lackofplanninginarithmetic/reasoningproblems 778.3Lackofplanningintextgeneration 789Societalinfluences829.1Challengesoferroneousgenerations 829.2Misinformationandmanipulation 839.3Bias 869.4Humanexpertise,jobs,andeconomics 899.5Constellationofinfluencesandconsiderations 9010DirectionsandConclusions9210.1Definitionsofintelligence,AI,andAGI 9210.2Onthepathtomoregeneralartificialintelligence 9310.3Whatisactuallyhappening? 94AGPT-4hascommonsensegrounding101BAppendixformultimodalandinterdisciplinarycomposition105B.1Furtherdetailsonintegrativeabilityresults 105B.2Furtherdetailsonvisionresults 108B.3Graphicnoveldesignexample 1103CAppendixfortheCodingsection111C.1MeasuringhumanperformanceonLeetCode 111C.2ExampleofGPT-4visualizingIMDbdata 112C.3Moreexamplesonvisualization 115C.4Examplefor2DHTMLgamedevelopment 116C.5Exampleforgraphicaluserinterfaceprogramming 116C.6Exampleforreverseengineering 119C.7TestingGPT-4’sabilitytoexecute(pseudo)code 121DAdditionalexamplesformathematicalreasoning122D.1Limitations 122D.2Furtherexamples 126D.3GeneratingmathproblemswithGPT-4 138D.4Mitigatingcalculationerrorsviaexternalcodeexecution 139EAdditionalInterpretabilityExamples141E.1ExplanationAgentMismatches 141FAdditionalexamplesforinteractionwiththeworld144F.1Interactwithtools 144F.2Examplesforinteractionwithenvironments 1494Somethingunknownisdoingwedon’tknowwhat.–SirArthurEddington1IntroductionIntelligenceisamultifacetedandelusiveconceptthathaslongchallengedpsychologists,philosophers,andcomputerscientists.Anattempttocaptureitsessencewasmadein1994byagroupof52psychologistswhosignedontoabroaddefinitionpublishedinaneditorialaboutthescienceofintelligence[Got97].Theconsensusgroupdefinedintelligenceasaverygeneralmentalcapabilitythat,amongotherthings,involvestheveproblemsthinkabstractlycomprehendcomplexideaslearnquicklyandlearnfromexperience.Thisdefinitionimpliesthatintelligenceisnotlimitedtoaspecificdomainortask,butratherencompassesabroadrangeofcognitiveskillsandabilities.Buildinganartificialsystemthatexhibitsthekindofgeneralintelligencecapturedbythe1994consensusdefinitionisalong-standingandambitiousgoalofAIresearch.Inearlywritings,thefoundersofthemoderndisciplineofartificialintelligence(AI)researchcalledoutsetsofaspirationalgoalsforunderstandingintelligence[MMRS06].Overdecades,AIresearchershavepursuedprinciplesofintelligence,includinggeneralizablemechanismsforreasoning(e.g.,[NSS59],[LBFL93])andconstructionofknowledgebasescontaininglargecorporaofcommonsenseknowledge[Len95].However,manyofthemorerecentsuccessesinAIresearchcanbedescribedasbeingnarrowlyfocusedonwell-definedtasksandchallenges,suchasplayingchessorGo,whichweremasteredbyAIsystemsin1996and2016,respectively.Inthelate-1990sandintothe2000s,therewereincreasingcallsfordevelopingmoregeneralAIsystems(e.g.,[SBD+96])andscholarshipinthefieldhassoughttoidentifyprinciplesthatmightunderlymoregenerallyintelligentsystems(e.g.,[Leg08,GHT15]).Thephrase,“artificialgeneralintelligence”(AGI),waspopularizedintheearly-2000s(see[Goe14])toemphasizetheaspirationofmovingfromthe“narrowAI”,asdemonstratedinthefocused,real-worldapplicationsbeingdeveloped,tobroadernotionsofintelli-gence,harkeningbacktothelong-termaspirationsanddreamsofearlierAIresearch.WeuseAGItorefertosystemsthatdemonstratebroadcapabilitiesofintelligenceascapturedinthe1994definitionabove,withtheadditionalrequirement,perhapsimplicitintheworkoftheconsensusgroup,thatthesecapabilitiesareatorabovehuman-level.WenotehoweverthatthereisnosingledefinitionofAGIthatisbroadlyaccepted,andwediscussotherdefinitionsintheconclusionsection.ThemostremarkablebreakthroughinAIresearchofthelastfewyearshasbeentheadvancementofnaturallanguageprocessingachievedbylargelanguagemodels(LLMs).TheseneuralnetworkmodelsarebasedontheTransformerarchitecture[VSP+17]andtrainedonmassivecorporaofweb-textdata,usingatitscoreaself-supervisedobjectiveofpredictingthenextwordinapartialsentence.Inthispaper,wereportonevidencethatanewLLMdevelopedbyOpenAI,whichisanearlyandnon-multimodalversionofGPT-4[Ope23],exhibitsmanytraitsofintelligence,accordingtothe1994definition.Despitebeingpurelyalanguagemodel,thisearlyversionofGPT-4demonstratesremarkablecapabilitiesonavarietyofdomainsandtasks,includingabstraction,comprehension,vision,coding,mathematics,medicine,law,understandingofhumanmotivesandemotions,andmore.WeinteractedwithGPT-4duringitsearlydevelopmentbyOpenAIusingpurelynaturallanguagequeries(prompts)1.InFigure1.1,wedisplaysomepreliminaryexamplesofoutputsfromGPT-4,askingittowriteaproofofinfinitudeofprimesintheformofapoem,todrawaunicorninTiKZ(alanguageforcreatinggraphicsinLATEX),tocreateacomplexanimationinPython,andtosolveahigh-schoollevelmathematicalproblem.Iteasilysucceedsatallthesetasks,andproducesoutputsthatareessentiallyindistinguishablefrom(orevenbetterthan)whathumanscouldproduce.WealsocompareGPT-4’sperformancetothoseofpreviousLLMs,mostnotablyChatGPT,whichisafine-tunedversionof(animproved)GPT-3[BMR+20].InFigure1.2,wedisplaytheresultsofaskingChatGPTforboththeinfini-tudeofprimespoemandtheTikZunicorndrawing.Whilethesystemperformsnon-triviallyonbothtasks,thereisnocomparisonwiththeoutputsfromGPT-4.Thesepreliminaryobservationswillrepeatthemselvesthroughoutthepaper,onagreatvarietyoftasks.ThecombinationofthegeneralityofGPT-4’scapabilities,withnumerousabilitiesspanningabroadswathofdomains,anditsperformanceonawidespectrumoftasksatorbeyondhuman-level,makesuscomfortablewithsayingthatGPT-4isasignificantsteptowardsAGI.1AsGPT-4’sdevelopmentcontinuedafterourexperiments,oneshouldexpectdifferentresponsesfromthefinalversionofGPT-4.Inparticular,allquantitativeresultsshouldbeviewedasestimatesofthemodel’spotential,ratherthandefinitivenumbers.Werepeatthiscaveatthroughoutthepapertoclarifythattheexperienceonthedeployedmodelmaydiffer.Moreoverweemphasizethattheversionwetestedwastext-onlyforinputs,butforsimplicitywerefertoitasGPT-4too.GPT-4Prompt:Canyouwriteaproofthatthereareinfinitelymanyprimes,witheverylinethatrhymes?GPT-4:Yes,IthinkIcan,Thoughitmighttakeacleverplan.I’llstartbynotingEuclid’sproof,Whichshowsthatprimesaren’tjustaloof.Assumewehaveafinitelist,Ofprimes,andthatnonehavebeenmissed.Multiplythemalltogether,Andaddone,justtobeclever.Theresultwillbeanumber,Thathasnoprimefactors,ohwonder!Buteverynumberhasaprime,Thatdividesit,it’sjustamatteroftime.Sowe’vefoundacontradiction,Andourfinitelistneedseviction.Theremustbeinfinitelymanyprimes,Andthat’stheendofmyrhyminglines.sPrompt:DrawaunicorninTiKZ.GPT-4:[ProducesLATEXcompilingtofollowingpicture.]Prompt:Canyougenerateapyplotforthefollowingdata:x=[1,3,5,6,8],y1=[2,3,5,18,1],y2=[3,5,6,8,1],y3=[5,1,2,3,4],y4=[9,7,2,3,1].Iwanty1,y2tobeinthesameplot,buty3isinanotherplotnexttothatplot,y4isinbelow.Iwantthelegendofy1tobe”bob”,y2tobe”alice”,y3tobe”bilbo”,y4tobe”allie”.Iwantthex-axistobelabeledwith”time”andyaxistobelabeledwith”money”.Iwanttoadda10%-40%randomerrorbartoeachcurve,throughalltimes(includingnon-integers).Iwantsmoothedcurvestoshowtheplot,andsmoothederrorbar.Donotuselinearinterpolation,usesmoothinterpolation!Iwanttoalsoaddsomesmallzig-zagtothesmoothedcurvetomakeitlookmorereal.Iwanttoputabaselineasthemeanofeachline.Iwanttoputapiechartbelowindicatingthefractionofthefourpeopleineachtimestep.Ialsowantananimationtoshowhowthefractionsarechanginginthepiechartin**continuoustime**.Interpolatethemissingfractions!Ialsowantverticallineanimationinotherthreeplotstomatchthepiechart.Iwantthefanciestplot.Pleaseaddasmanyfancythingsaspossible.Hereisapossiblecodetogenerateapyplotforthefollowingdata,withsomecommentstoexplainthesteps:GPT-4:[Wegivetwosnapshotsfromtheanimationresultingfromtheproducedcode]Prompt:Supposeg(x)=f_1(x),g(0)=5,g(4)=7,g(3)=2,g(7)=9,g(9)=6whatisf(f(f(6)))?GPT-4:Therearedifferentwaystoapproachthisproblem,butonepossiblemethodistousethefactthatg(x)=f_1(x)impliesthatf(g(x))=xforanyxinthedomainofg.Thatis,applyingftobothsidesoftheequationg(x)=f_1(x)givesf(g(x))=f(f_1(x))=x.Thismeansthatwecanundotheeffectoffbyapplyingg,andviceversa.Usingthisidea,wecantrytofindf(6)bylookingforthevalueofxsuchthatg(x)=6.Fromthegiveninformation,weseethatg(9)=6,sof(6)=f(g(9))=9.Similarly,wecanfindf(9)bylookingforthevalueofxsuchthatg(x)=9.Weseethatg(7)=9,sof(9)=f(g(7))=7.Finally,wecanfindf(7)bylookingforthevalueofxsuchthatg(x)=7.Weseethatg(4)=7,sof(7)=f(g(4))=4.Therefore,f(f(f(6)))=f(f(9))=f(7)=4.Figure1.1:PreliminaryexamplesofGPT-4’scaabilitiesinlanguage,vision,coding,andmathe-matics.6ChatGPTChatGPTPrompt:Canyouwriteaproofthatthereareinfinitelymanyprimes,witheverylinethatrhymes?ChatGPT:Sure!Hereisaproofthatthereareinfinitelymanyprimes,witheachlineoftheproofrhyming:Considerthesetofallprimes.CallthissetPandletitstartwithjusttwo,thefirstprime.IfwetakealltheprimesinPandaddone,Wegetanewsetofnumbersthathasneverbeendone.Thisset,callitS,iscomposedofonlycompositenumbers.Noprimeswillbefoundamongthem,that’sforsure.Butweknowthattheremustbeatleastoneprime,SinceSisinfinite,andthatmuchisclear.IfwetakethesmallestnumberinSthatisnotinP,Andcallitp,wecanaddittoourset,don’tyousee?Butthisprocesscanberepeatedindefinitely.Thus,oursetPmustalsobeinfinite,you’llagree.Sothereyouhaveit,aproofthat’squitesolid.Thereareinfinitelymanyprimes,thatmuchwecansee.Noneedtobecoy,noneedtobecontrite.Infinityisabignumber,justlikeP.Prompt:DrawaunicorninTiKZ.ChatGPT:[ProducesLATEXcompilingtofollowingpicture.]Figure1.2:ChatGPToutputonthefirsttwopromptsfromFigure1.1.OurclaimthatGPT-4representsprogresstowardsAGIdoesnotmeanthatitisperfectatwhatitdoes,orthatitcomesclosetobeingabletodoanythingthatahumancando(whichisoneoftheusualdefinitionofAGI;seetheconclusionsectionformoreonthis),orthatithasinnermotivationandgoals(anotherkeyaspectinsomedefinitionsofAGI).Infact,evenwithintherestrictedcontextofthe1994definitionofintelligence,itisnotfullyclearhowfarGPT-4cangoalongsomeofthoseaxesofintelligence,e.g.,planning(seeSection8),andarguablyitisentirelymissingtheparton“learnquicklyandlearnfromexperience”asthemodelisnotcontinuouslyupdating(althoughitcanlearnwithinasession,seeSection5forexample).OverallGPT-4stillhasmanylimitations,andbiases,whichwediscussindetailbelowandthatarealsocoveredinOpenAI’sreport[Ope23].Inparticularitstillsuffersfromsomeofthewell-documentedshortcomingsofLLMssuchastheproblemofhallucinations[MNBM20](seeFigure1.8)ormakingbasicarithmeticmistakes[CKB+21](seeAppendixD),andyetithasalsoovercomesomefundamentalobstaclessuchasacquiringmanynon-linguisticcapabilities(e.g.,itsolvesmostoftheLLMfailuremodesdescribedin[MIB+23],anditalsomadegreatprogressoncommon-sense,seeFigure1.7forafirstexampleandAppendixAformore).Thishighlightsthefactthat,whileGPT-4isatorbeyondhuman-levelformanytasks,overallitspatternsofintelligencearedecidedlynothuman-like.However,GPT-4isalmostcertainlyonlyafirststeptowardsaseriesofincreasinglygenerallyintelligentsystems,andinfactGPT-4itselfhasimprovedthroughoutourtimetestingit,seeFigure1.3fortheevolutionoftheunicorndrawingoverthecourseofamonthoftraining2.Evenasafirststep,however,GPT-4challengesaconsiderablenumberofwidelyheldassumptionsaboutmachineintelligence,andexhibitsemergentbehaviorsandcapabilitieswhosesourcesandmechanismsare,atthismoment,hardtodiscernprecisely(seeagaintheconclusionsectionformorediscussiononthis).OurprimarygoalincomposingthispaperistoshareourexplorationofGPT-4’scapabilitiesandlimitationsinsupportofourassessmentthatatechnologicalleaphasbeenachieved.WebelievethatGPT-4’sintelligencesignalsatrueparadigmshiftinthefieldofcomputerscienceandbeyond.2Notethattheimprovingwerefertohereisaslowtypeoflearning,whicheventuallycomestoahalt,asopposedtothefast-pacedandreal-timelearningonewouldexpectfromanAGI.7Figure1.3:WequeriedGPT-4threetimes,atroughlyequaltimeintervalsoverthespanofamonthwhilethesystemwasbeingrefined,withtheprompt“DrawaunicorninTikZ”.WecanseeaclearevolutioninthesophisticationofGPT-4’sdrawings.1.1OurapproachtostudyingGPT-4’sintelligenceHowcanwemeasuretheintelligenceofanLLMthathasbeentrainedonanunknownbutextremelyvastcorpusofweb-textdata?Thestandardapproachinmachinelearningistoevaluatethesystemonasetofstandardbenchmarkdatasets,ensuringthattheyareindependentofthetrainingdataandthattheycoverarangeoftasksanddomains.Thisapproachisdesignedtoseparatetruelearningfrommerememorization,andisbackedupbyarichtheoreticalframework[SSBD14,MRT18].However,thismethodologyisnotnecessarilysuitableforstudyingGPT-4,fortworeasons.First,sincewedonothaveaccesstothefulldetailsofitsvasttrainingdata,wehavetoassumethatithaspotentiallyseeneveryexistingbenchmark,oratleastsomesimilardata.Forexample,itseemslikeGPT-4knowstherecentlyproposedBIG-bench[SRR+22](atleastGPT-4knowsthecanaryGUIDfromBIG-bench).Ofcourse,OpenAIthemselveshaveaccesstoallthetrainingdetails,andthustheirreport[Ope23]containsalotofdetailedbenchmarkresults.Nevertheless,thesecondreasonforgoingbeyondtraditionalbenchmarksisprobablymoresignificant:OneofthekeyaspectsofGPT-4’sintelligenceisitsgenerality,theabilitytoseeminglyunderstandandconnectanytopic,andtoperformtasksthatgobeyondthetypicalscopeofnarrowAIsystems.SomeofGPT-4’smostimpressiveperformanceareontasksthatdonotadmitasinglesolution,suchaswritingagraphicuserinterface(GUI)orhelpingahumanbrainstormonsomework-relatedproblem.Benchmarksforsuchgenerativeorinteractivetaskscanbedesignedtoobutthemetricofevaluationbecomesachallenge(seee.g.,[PSZ+21]forsomerecentprogressonthisactiveresearchareainNLP).WenotethatcriticismsofthestandardapproachtomeasureAIsystemswerealsomadein[Cho19],whereanewbenchmarkwasproposedtoevaluategeneralintelligence.WedonottestGPT-4onthelatterbenchmarkforthereasonspreviouslymentioned,aswellasthefactthatthebench-markisvisualinnatureandthusmoreappropriateforthemultimodalversionofGPT-4describedin[Ope23].Toovercomethelimitationsdescribedabove,weproposehereadifferentapproachtostudyingGPT-4whichisclosertotraditionalpsychologyratherthanmachinelearning,leveraginghumancreativityandcu-riosity.WeaimtogeneratenovelanddifficulttasksandquestionsthatconvincinglydemonstratethatGPT-4goesfarbeyondmemorization,andthatithasadeepandflexibleunderstandingofconcepts,skills,anddomains(asomewhatsimilarapproachwasalsoproposedin[CWF+22]).WealsoaimtoprobeGPT-4’sresponsesandbehaviors,toverifyitsconsistency,coherence,andcorrectness,andtouncoveritslimitationsandbiases.Weacknowledgethatthisapproachissomewhatsubjectiveandinformal,andthatitmaynotsatisfytherigorousstandardsofscientificevaluation.However,webelievethatitisausefulandnecessaryfirststeptoappreciatetheremarkablecapabilitiesandchallengesofGPT-4,andthatsuchafirststepopensupnewopportunitiesfordevelopingmoreformalandcomprehensivemethodsfortestingandanalyzingAIsystemswithmoregeneralintelligence.ToillustrateourapproachtoassessingGPT-4’sintelligence,letusconsiderthefirsttwoexampleinter-actionswithGPT-4thatwehaveinFigure1.1.ThefirstexampleisaskingGPT-4towriteaproofoftheinfinitudeofprimesintheformofapoem.Thisisachallengingtaskthatrequirescombiningelementary8Figure1.4:WegavetoGPT-4atransformedversionoftheTikZc