让大模型自己生成工具，动态解决复杂任务。
Advanced Tool Learning and Selection System (ATLASS): A Closed-Loop Framework Using LLM
- 大模型自主生成外部工具，无需预设工具集。
- 通过环境自动配置与API文档调用，生成可运行的复杂工具。
- 适合需要灵活应对新问题的研究者与开发者。
将大语言模型（LLM）代理与外部工具结合，可使模型解决超出自身知识范围的复杂任务。然而，人工设计的工具灵活性差，仅限于专家预设的工具范围。为解决此问题，我们提出ATLASS——一种闭环框架，使LLM能按需动态生成外部工具。该框架包含三个阶段：第一阶段为理解工具需求，代理判断是否需要工具并明确功能；第二阶段为工具检索或生成，根据可用性选择或创建工具；第三阶段为任务求解，整合所需组件完成任务。工具数据集存储生成的工具，实现复用并降低推理成本。现有系统在生成需API或外部包的复杂工具时表现不佳。ATLASS通过自动配置环境、在线获取API文档，并利用Python解释器生成可靠且适用广泛的工具来克服这一缺陷。本研究采用OpenAI GPT-4.0作为代理，通过人类反馈确保生成代码的安全性与伦理性。ATLASS突破了预设工具集的局限，显著提升适应能力，为复杂问题求解提供了真实可行的动态工具支持。
原文摘要 · Abstract (English)
The combination of LLM agents with external tools enables models to solve complex tasks beyond their knowledge base. Human-designed tools are inflexible and restricted to solutions within the scope of pre-existing tools created by experts. To address this problem, we propose ATLASS, an advanced tool learning and selection system designed as a closed-loop framework. It enables the LLM to solve problems by dynamically generating external tools on demand. In this framework, agents play a crucial role in orchestrating tool selection, execution, and refinement, ensuring adaptive problem-solving capabilities. The operation of ATLASS follows three phases: The first phase, Understanding Tool Requirements, involves the Agents determining whether tools are required and specifying their functionality; the second phase, Tool Retrieval/Generation, involves the Agents retrieving or generating tools based on their availability; and the third phase, Task Solving, involves combining all the component tools necessary to complete the initial task. The Tool Dataset stores the generated tools, ensuring reusability and minimizing inference cost. Current LLM-based tool generation systems have difficulty creating complex tools that need APIs or external packages. In ATLASS, we solve the problem by automatically setting up the environment, fetching relevant API documentation online, and using a Python interpreter to create a reliable, versatile tool that works in a wider range of situations. OpenAI GPT-4.0 is used as the LLM agent, and safety and ethical concerns are handled through human feedback before executing generated code. By addressing the limitations of predefined toolsets and enhancing adaptability, ATLASS serves as a real-world solution that empowers users with dynamically generated tools for complex problem-solving.
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