增强大语言模型的函数调用能力：提示格式、数据集成与多语言翻译策略
Enhancing Function-Calling Capabilities in LLMs: Strategies for Prompt Formats, Data Integration, and Multilingual Translation
AI总结：
本研究通过探索提示格式、数据集成、决策标记、思维链推理及翻译流水线等策略，增强LLMs的函数调用能力，发现指令遵循数据可提升准确率与相关性检测，决策标记及翻译流水线也能带来显著改进。
摘要：
大语言模型（LLMs）显著推动了自主智能体的发展，尤其在零样本工具使用（也称为函数调用）方面。本研究通过探索不同方法深入增强LLMs的函数调用能力，包括集成函数描述的提示格式、混合函数调用与指令遵循数据、引入用于条件提示的新型决策标记（Decision Token）、利用思维链推理，以及通过翻译流水线克服多语言挑战。关键发现与贡献如下：（1）指令遵循数据提升函数调用准确率与相关性检测能力；（2）新提出的决策标记结合合成非函数调用数据增强相关性检测；（3）定制化翻译流水线有效克服多语言局限，在繁体中文上展现显著提升。这些见解凸显了提升LLMs函数调用能力与多语言应用的潜力。
英文摘要：
Large language models (LLMs) have significantly advanced autonomous agents, particularly in zero-shot tool usage, also known as function calling. This research delves into enhancing the function-calling capabilities of LLMs by exploring different approaches, including prompt formats for integrating function descriptions, blending function-calling and instruction-following data, introducing a novel Decision Token for conditional prompts, leveraging chain-of-thought reasoning, and overcoming multilingual challenges with a translation pipeline. Our key findings and contributions are as follows: (1) Instruction-following data improves both function-calling accuracy and relevance detection. (2) The use of the newly proposed Decision Token, combined with synthetic non-function-call data, enhances relevance detection. (3) A tailored translation pipeline effectively overcomes multilingual limitations, demonstrating significant improvements in Traditional Chinese. These insights highlight the potential for improved function-calling capabilities and multilingual applications in LLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
