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    <abstract>While enabling large language models to implement function calling (known as APIs) can greatly enhance the performance of Large Language Models (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 introduces “Reverse Chain”, a controllable, target-driven approach designed to empower LLMs with the capability to operate external APIs only via prompts. Recognizing that most LLMs have limited tool-use capabilities, Reverse Chain limits LLMs to executing simple tasks, e.g., API Selection and Argument Completion. Furthermore, to manage a controllable multi-function calling, Reverse Chain adopts a generic rule-based on a backward reasoning process. This rule determines when to do API selection or Argument completion. To evaluate the multi-tool-use capability of LLMs, we have released a compositional multi-tool task dataset, available at https://github.com/zhangyingerjelly/reverse-chain. Extensive numerical experiments validate the remarkable proficiency of Reverse Chain in managing multiple API calls.</abstract>
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%0 Conference Proceedings
%T Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning
%A Zhang, Yinger
%A Cai, Hui
%A Song, Xierui
%A Chen, Yicheng
%A Sun, Rui
%A Zheng, Jing
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F zhang-etal-2024-reverse
%X While enabling large language models to implement function calling (known as APIs) can greatly enhance the performance of Large Language Models (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 introduces “Reverse Chain”, a controllable, target-driven approach designed to empower LLMs with the capability to operate external APIs only via prompts. Recognizing that most LLMs have limited tool-use capabilities, Reverse Chain limits LLMs to executing simple tasks, e.g., API Selection and Argument Completion. Furthermore, to manage a controllable multi-function calling, Reverse Chain adopts a generic rule-based on a backward reasoning process. This rule determines when to do API selection or Argument completion. To evaluate the multi-tool-use capability of LLMs, we have released a compositional multi-tool task dataset, available at https://github.com/zhangyingerjelly/reverse-chain. Extensive numerical experiments validate the remarkable proficiency of Reverse Chain in managing multiple API calls.
%U https://aclanthology.org/2024.findings-naacl.22
%P 302-325

Markdown (Informal)

[Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning](https://aclanthology.org/2024.findings-naacl.22) (Zhang et al., Findings 2024)

• Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning (Zhang et al., Findings 2024)

ACL

• Yinger Zhang, Hui Cai, Xierui Song, Yicheng Chen, Rui Sun, and Jing Zheng. 2024. Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 302–325, Mexico City, Mexico. Association for Computational Linguistics.