CRITICTOOL: Evaluating Self-Critique Capabilities of Large Language
  Models in Tool-Calling Error Scenarios
Authors: name: Shiting Huang, name: Zhen Fang, name: Zehui Chen, name: Siyu Yuan, name: Junjie Ye, name: Yu Zeng, name: Lin Chen, name: Qi Mao, name: Feng Zhao

The ability of large language models (LLMs) to utilize external tools has
enabled them to tackle an increasingly diverse range of tasks. However, as the
tasks become more complex and long-horizon, the intricate tool utilization
process may trigger various unexpected errors. Therefore, how to effectively
handle such errors, including identifying, diagnosing, and recovering from
them, has emerged as a key research direction for advancing tool learning. In
this work, we first extensively analyze the types of errors encountered during
the function-calling process on several competitive tool evaluation benchmarks.
Based on it, we introduce CRITICTOOL, a comprehensive critique evaluation
benchmark specialized for tool learning. Building upon a novel evolutionary
strategy for dataset construction, CRITICTOOL holds diverse tool-use errors
with varying complexities, which better reflects real-world scenarios. We
conduct extensive experiments on CRITICTOOL, and validate the generalization
and effectiveness of our constructed benchmark strategy. We also provide an
in-depth analysis of the tool reflection ability on various LLMs, offering a
new perspective on the field of tool learning in LLMs. The code is available at
https://github.com/Shellorley0513/CriticTool{https://github.com/Shellorley0513/CriticTool}.