Toolsandbox: A Stateful, Conversational, Interactive Evaluation Benchmark For LLM Tool Use Capabilities

Lu Jiarui, Holleis Thomas, Zhang Yizhe, Aumayer Bernhard, Nan Feng, Bai Felix, Ma Shuang, Ma Shen, Li Mengyu, Yin Guoli, Wang Zirui, Pang Ruoming. Arxiv 2024

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Has Code Prompting Reinforcement Learning Tools

Recent large language models (LLMs) advancements sparked a growing research interest in tool assisted LLMs solving real-world challenges, which calls for comprehensive evaluation of tool-use capabilities. While previous works focused on either evaluating over stateless web services (RESTful API), based on a single turn user prompt, or an off-policy dialog trajectory, ToolSandbox includes stateful tool execution, implicit state dependencies between tools, a built-in user simulator supporting on-policy conversational evaluation and a dynamic evaluation strategy for intermediate and final milestones over an arbitrary trajectory. We show that open source and proprietary models have a significant performance gap, and complex tasks like State Dependency, Canonicalization and Insufficient Information defined in ToolSandbox are challenging even the most capable SOTA LLMs, providing brand-new insights into tool-use LLM capabilities. ToolSandbox evaluation framework is released at https://github.com/apple/ToolSandbox

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