Tool Playgrounds: A Comprehensive and Analyzable Benchmark for LLM Tool Invocation

Abstract

The rapid advancement of large language models (LLMs) has paved the way for their use in solving real-world problems, which in turn has significantly driven the development of tool-assisted LLMs. This progress necessitates thorough evaluation methods. However, existing benchmarks typically only provide end-to-end scores but lack in-depth analysis and often suffer from issues such as instability. To address this gap, we have meticulously designed the Tool Playgrounds framework, a comprehensive, analyzable, and extensible benchmark. This framework evaluates boundary dimensions such as parameter missing interaction, parameter correction, tool failover, and leveraging internal knowledge. Our findings indicate that even the most advanced commercial models frequently overlook these essential aspects and face challenges in managing complex tool usage. To foster further research and development, we have made our code, dataset, and leaderboard publicly available on https://github.com/zhiwei-dong/ToolPlaygrounds.