SwissNYF：面向黑盒环境的工具增强LLM智能体
SwissNYF: Tool Grounded LLM Agents for Black Box Setting
AI总结：
针对不可逆API等黑盒环境下的工具规划难题，提出利用LLM程序合成能力的TOPGUN方法及配套的SwissNYF套件，确保方案在实施前得到验证，提升复杂API交互的有效性。
摘要：
尽管大型语言模型（LLMs）在函数调用方面展现出增强的能力，但这些进步主要依赖于获取函数的响应。这种方法对于较简单的API很实用，但在面对会对系统产生重大影响的不可逆API（如数据库删除API）时，会面临可扩展性问题。同样，每次API调用需要大量时间的过程，以及需要前瞻性规划的过程（如自动化动作流水线），也带来了复杂的挑战。此外，由于算法无法直接访问这些函数的具体实现或使用它们的密钥，经常出现需要通用化方法的场景。传统的工具规划方法在这些情况下是不够的，迫使需要在黑盒环境中操作。与它们在工具操作中的表现不同，LLMs在黑盒任务中表现出色，如程序合成。因此，我们利用LLMs的程序合成能力，在黑盒环境中制定工具使用策略，确保解决方案在实施前得到验证。我们引入了TOPGUN，一种巧妙设计的方法，利用程序合成进行黑盒工具规划。同时配备了SwissNYF，这是一个集成了黑盒算法用于规划和验证任务的综合套件，解决了上述挑战，并增强了LLMs在复杂API交互中的多功能性和有效性。SwissNYF的公开代码可在 https://github.com/iclr-dummy-user/SwissNYF 获取。
英文摘要：
While Large Language Models (LLMs) have demonstrated enhanced capabilities in function-calling, these advancements primarily rely on accessing the functions' responses. This methodology is practical for simpler APIs but faces scalability issues with irreversible APIs that significantly impact the system, such as a database deletion API. Similarly, processes requiring extensive time for each API call and those necessitating forward planning, like automated action pipelines, present complex challenges. Furthermore, scenarios often arise where a generalized approach is needed because algorithms lack direct access to the specific implementations of these functions or secrets to use them. Traditional tool planning methods are inadequate in these cases, compelling the need to operate within black-box environments. Unlike their performance in tool manipulation, LLMs excel in black-box tasks, such as program synthesis. Therefore, we harness the program synthesis capabilities of LLMs to strategize tool usage in black-box settings, ensuring solutions are verified prior to implementation. We introduce TOPGUN, an ingeniously crafted approach leveraging program synthesis for black box tool planning. Accompanied by SwissNYF, a comprehensive suite that integrates black-box algorithms for planning and verification tasks, addressing the aforementioned challenges and enhancing the versatility and effectiveness of LLMs in complex API interactions. The public code for SwissNYF is available at https://github.com/iclr-dummy-user/SwissNYF.
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。
