教大模型用外部工具，提升解决复杂任务的能力。
LLM With Tools: A Survey
- 提出标准化工具集成框架，按指令生成并执行行动计划。
- 实证显示工具增强后模型在ScienceQA上表现显著提升。
- 适合研究大模型应用与智能系统构建的开发者和学者。
将外部工具引入大语言模型以增强其处理特定复杂任务的效率与准确性，是一项新兴方法。本文系统探讨了指导大模型使用外部工具的方法、挑战与进展，提出了一个基于函数映射的标准化工具集成范式，强调理解用户意图、工具选择与动态计划调整的重要性。研究揭示了工具调用时机、选择准确率及鲁棒推理过程等关键挑战，并在微调与上下文学习范式下探索了提升多样性、扩充数据集与增强泛化能力的创新技术。此外，本文还探讨了让大模型不仅使用工具，还能自主创建工具的可能性，或可重塑其从工具使用者向创造者的角色转变。最后，我们在ScienceQA数据集上复现了Chameleon的实验结果，并分析了其代码结构。
原文摘要 · Abstract (English)
The integration of tools in augmenting large language models presents a novel approach toward enhancing the efficiency and accuracy of these models in handling specific, complex tasks. This paper delves into the methodology,challenges, and developments in the realm of teaching LLMs to use external tools, thereby pushing the boundaries of their capabilities beyond pre-existing knowledge bases. We introduce a standardized paradigm for tool integration guided by a series of functions that map user instructions to actionable plans and their execution, emphasizing the significance of understanding user intent, tool selection, and dynamic plan adjustment. Our exploration reveals the various challenges encountered, such as tool invocation timing, selection accuracy, and the need for robust reasoning processes. In addressing these challenges, we investigate techniques within the context of fine-tuning and incontext learning paradigms, highlighting innovative approaches to ensure diversity, augment datasets, and improve generalization.Furthermore, we investigate a perspective on enabling LLMs to not only utilize but also autonomously create tools, which may redefine their role from mere tool users to tool creators. Finally,we reproduced Chameleon's results on ScienceQA and analyzed the code structure.
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