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While enabling large language models to implement function calling (known asAPIs) can greatly enhance the performance of LLMs, function calling is still achallenging task due to the complicated relations between different APIs,especially in a context-learning setting without fine-tuning. This paperproposes a simple yet controllable target-driven approach called Reverse Chainto empower LLMs with capabilities to use external APIs with only prompts. Giventhat 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., APIselection and argument completion, and a generic rule is employed to implementa controllable multiple functions calling. In this generic rule, afterselecting a final API to handle a given task via LLMs, we first ask LLMs tofill the required arguments from user query and context. Some missing argumentscould be further completed by letting LLMs select another API based on APIdescription before asking user. This process continues until a given task iscompleted. Extensive numerical experiments indicate an impressive capability ofReverse 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.

Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning