@article{JIANG2025, 
author = {ZhiYing JIANG and ZhenYu HUANG and ChenWei SONG and ZeYu ZHANG and ZhongHe HAN and WeiWei GU and QiHang GONG and SiYe LIU and Yan ZHAO},
title = {Multi-tool coordinated call with reasoning to annotation and coding prompt paradigm based on large language model},
year = {2025},
journal = {Journal of Beijing University of Chemical Technology (Natural Science Edition)},
volume = {52},
number = {3},
pages = {105-113},
keywords = {Reasoning to Annotation and Coding (ReACo), tool invocation, chain-of-thought, large language model},
url = {https://www.sciopen.com/article/10.13543/j.bhxbzr.2025.03.011},
doi = {10.13543/j.bhxbzr.2025.03.011},
abstract = {Because large language models show good understanding and generation capabilities, various industries have begun to study automated intelligent assistants based on these models, calling upon tools to help people solve complex problems. Large language models are prone to hallucination problems when generating responses due to the closed and complex data in the training phase. When facing complex problem planning, large language models often find it difficult to accurately generate function names and parameters when calling multiple different tools, and cannot coordinate the invocation of multiple tools to find answers. To improve the model’s tool call accuracy when planning tasks for complex problems, this paper proposes a prompt method that employs a chain-of-thought approach, Reasoning to Annotation and Coding (ReACo). This method fully utilizes pre-trained data and enhances the language model’s ability to understand complex tasks through a task planning prompt method that combines code and annotations. Based on this, a new framework for large language model thinking prompts, named ReACoGPT, is proposed. The language model using ReACoGPT prompts can accurately call multiple plug-ins and provide logical task planning capabilities based on facts, so that, while maintaining the logic of task planning, the actual requirements data can also be accurately utilized. Experimental results show that compared with existing methods, ReACoGPT has achieved improvements in multiple indicators on the RestBench dataset, confirming that the ReACo prompt method can enhance the planning and reasoning capabilities of large language models, effectively utilize a large amount of training data to effectively plan tasks, and promote the further development of large language models in tool learning.}
}