Summary of Toolsandbox: a Stateful, Conversational, Interactive Evaluation Benchmark For Llm Tool Use Capabilities, by Jiarui Lu et al.
ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities
by Jiarui Lu, Thomas Holleis, Yizhe Zhang, Bernhard Aumayer, Feng Nan, Felix Bai, Shuang Ma, Shen Ma, Mengyu Li, Guoli Yin, Zirui Wang, Ruoming Pang
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The recent advancements in large language models (LLMs) have sparked a growing interest in using tools to solve real-world challenges. To comprehensively evaluate the capabilities of tool-assisted LLMs, researchers need a comprehensive evaluation framework that includes stateful tool execution, implicit state dependencies between tools, and a built-in user simulator supporting on-policy conversational evaluation. The proposed ToolSandbox framework addresses this gap by providing a dynamic evaluation strategy for intermediate and final milestones over an arbitrary trajectory. The results show that open-source and proprietary models have a significant performance gap, and complex tasks like State Dependency, Canonicalization, and Insufficient Information are challenging even the most capable state-of-the-art (SOTA) LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can be very powerful tools for solving real-world problems. To make sure these models are working well, scientists need a special way to test them. This paper introduces a new testing framework called ToolSandbox that allows researchers to evaluate the capabilities of large language models in a more realistic and challenging way. |
