It is evident that the current state of Large Language Models (LLMs) necessitates the incorporation of external tools. The lack of straightforward algebraic and logical reasoning is well documented and prompted researchers to develop frameworks which allow LLMs to operate via external tools. The ontological nature of tool utilization for a specific task can be well formulated with a Directed Acyclic Graph (DAG). The central aim of the paper is to highlight the importance of graph based approaches to LLM-tool interaction in near future. We propose an exemplary framework to guide the orchestration of exponentially increasing numbers of external tools with LLMs,where objectives and functionalities of tools are graph encoded hierarchically. Assuming that textual segments of a Chain-of-Thought (CoT) can be imagined as a tool as defined here, the graph based framework can pave new avenues in that particular direction as well.

翻译：当前大语言模型在代数与逻辑推理能力上的明显缺陷，已促使研究者开发框架使其能够借助外部工具运行。针对特定任务的工具运用，其本体论本质可通过有向无环图进行有效建模。本文旨在强调基于图的方法在未来大语言模型-工具交互领域的重要性，并提出一个示范性框架，通过层级化编码工具目标与功能的有向图结构，引导大语言模型编排指数级增长的外部工具群。假设思维链中的文本片段可被视作本文定义的工具，该图基框架亦将为该研究方向开辟新路径。