While enabling large language models to implement function calling (known as
APIs) can greatly enhance the performance of LLMs, function calling is still a
challenging task due to the complicated relations between different APIs,
especially in a context-learning setting without fine-tuning. This paper
proposes a simple yet controllable target-driven approach called Reverse Chain
to empower LLMs with capabilities to use external APIs with only prompts. Given
that most open-source LLMs have limited tool-use or tool-plan capabilities,
LLMs in Reverse Chain are only employed to implement simple tasks, e.g., API
selection and argument completion, and a generic rule is employed to implement
a controllable multiple functions calling. In this generic rule, after
selecting a final API to handle a given task via LLMs, we first ask LLMs to
fill the required arguments from user query and context. Some missing arguments
could be further completed by letting LLMs select another API based on API
description before asking user. This process continues until a given task is
completed. Extensive numerical experiments indicate an impressive capability of
Reverse Chain on implementing multiple function calling. Interestingly enough,
the experiments also reveal that tool-use capabilities of the existing LLMs,
e.g., ChatGPT, can be greatly improved via Reverse Chain.

该论文提出了一种名为反向链的简单而可控的目标驱动方法，用于使大型语言模型在没有微调的情况下能够使用外部 API，通过实现简单的任务，如 API 选择和参数补全，并通过通用规则实现可控的多个函数调用。数值实验表明，反向链在实现多个函数调用方面具有令人印象深刻的能力，并且有趣的是，实验还表明现有语言模型的工具使用能力可以通过反向链得到极大改善。