3.6 Agent Applications
Instead of focusing solely on general language tasks, LLMs can be utilized as powerful tools
in various domains. Equipping LLMs with external tools can greatly expand the capabilities of
the model [151]. ToolLLM [152] provides a comprehensive framework to equip open-source large
language models with tool use capabilities. Huang et al. [71] introduced KOSMOS-1, which is capable
of understanding general patterns, following instructions, and learning based on context. The study
by MRKL Karpas et al. [83] emphasized the importance of understanding when and how to utilize
external symbolic tools, as this knowledge is dependent on the capabilities of LLMs, particularly
when these tools can reliably perform functions. Additionally, two other studies, Toolformer [163]
and TALM [143], explored the utilization of tools to enhance language models. Toolformer employs
a training approach to determine the optimal usage of specific APIs and integrates the obtained
ASurveyonEvaluationofLargeLanguageModels