Key Points
1. AnyTool is a new large language model agent designed to address user queries by leveraging over 16,000 APIs from Rapid API. It incorporates a hierarchical API retriever, a solver, and a self-reflection mechanism, all operating without the need for additional training.
2. The AnyTool agent outperforms strong baselines such as ToolLLM and a GPT-4 variant tailored for tool utilization by a significant margin, demonstrating its superiority in resolving user queries.
3. The hierarchical structure within the API retriever consists of three tiers, including a meta-agent, multiple category agents, and various tool agents, each with diverse roles.
4. A self-reflection mechanism significantly reduces the tendency to "oversearch" for simpler queries while providing a more context-rich and in-depth search for complex queries, enhancing the efficiency and effectiveness of the query resolution process.
5. The evaluation protocol used by AnyTool, termed AnyToolBench, revises the evaluation methodology to better reflect practical application scenarios, addressing a limitation in the previous evaluation protocol that led to artificially high pass rates.
6. AnyTool demonstrates the effectiveness of resolving user queries through various experiments on ToolBench and AnyToolBench, exhibiting its superior performance over established models.
7. An extensive ablation study demonstrates the positive effects of the hierarchical API retriever and the self-reflection feature on AnyTool's performance.
8. Several factors such as the size of the API pool, the maximal size of the API-candidate pool, and the presence of the tool agent in the API retriever have been studied to understand their impact on AnyTool's performance.
9. The paper highlights future research directions, including optimizing the organization of APIs for improved performance and developing an advanced open-source LLM specifically for API utilization.
Summary
The paper introduces "AnyTool," a large language model agent designed to utilize over 16,000 APIs from Rapid API in addressing user queries effectively. AnyTool consists of an API retriever with a hierarchical structure, a solver for resolving user queries, and a self-reflection mechanism for reactivating AnyTool if the initial solution is impracticable. The agent is powered by the function-calling feature of GPT-4, eliminating the need for external module training. The paper revisits the evaluation protocol of previous works and introduces a new benchmark called AnyToolBench. Experimental results demonstrate AnyTool's superiority over strong baselines such as ToolLLM and a specialized GPT-4 variant for tool utilization, with AnyTool outperforming ToolLLM by +35.4% in average pass rate on ToolBench.
Detailed Description of AnyTool
The paper further discusses the challenges of driving LLMs to effectively use tools, and introduces an agent, AnyTool, which is designed to leverage a vast array of tools to address user queries. It includes detailed explanations of the components and functionality of AnyTool, while also discussing the limitations of the evaluation protocols in previous works and the proposed improvements in the evaluation methodology. The paper concludes with an overview of the future research directions for optimizing the organization of APIs and developing an advanced open-source LLM specifically for API utilization.
Evaluation of AnyTool's Performance
The results indicate that AnyTool significantly enhances the effectiveness of resolving user queries through various tools, and its performance surpasses established models significantly. However, the paper acknowledges the need for verification of AnyTool's performance in extremely complex scenarios and suggests that the capabilities of GPT-4 also affect the feasibility of the solutions it generates.
In summary, the paper introduces AnyTool as an advanced agent for utilizing a vast array of APIs to effectively address user queries, and provides detailed insights into its architecture, performance, and future research directions.
Reference: https://arxiv.org/abs/2402.04253