ssinvolvesleveragingtherichknowledgeobtainedfromhumantool-useexperiences.Whendeployedinpractice,aﬁne-tunedmodelalleviatestheneedforincorporatingtooldeﬁnitionsintheinput,whichshrinkstheinputlengthandacceleratesmodelinference.However,amajorlimitationofﬁne-tuningisthatitoftenrequiresextensivehumanannotationsordemonstrations.Additionally,toolsarefrequentlyupdated,andﬁne-tunedmodelswillneedtoberetrainedwithupdatedexamples,incurringadditionalcosts.Morediscussionaboutthetuning-basedsolutionisleftin§3.3.3.2.2PlanningwithReasoningAsdiscussedin§3.2.1,understandingtheintentandtoolslaysasolidfoundationforplanning.Nevertheless,itisstillinsufﬁcientfortacklingintricatetasks.Theuserqueryqoftenimpliesacomplextaskthatshouldbedividedintomultiplesub-taskswithpropersequencing,therebynecessitatingaprocessofreasoning.Recentresearchhasrevealedthatreasoningcapabilitiescanemergewhenfoundationmodelsarescaleduptoacertainsize(Weietal.,2022b).Inparticular,foundationmodelswithtensorhundredsofbillionsofparameterscang
Tool Learning with Foundation Models