  Tool-augmented large language models (LLMs) are rapidly being integrated into
real-world applications. Due to the lack of benchmarks, the community still
needs to fully understand the hallucination issues within these models. To
address this challenge, we introduce a comprehensive diagnostic benchmark,
ToolBH. Specifically, we assess the LLM's hallucinations through two
perspectives: depth and breadth. In terms of depth, we propose a multi-level
diagnostic process, including (1) solvability detection, (2) solution planning,
and (3) missing-tool analysis. For breadth, we consider three scenarios based
on the characteristics of the toolset: missing necessary tools, potential
tools, and limited functionality tools. Furthermore, we developed seven tasks
and collected 700 evaluation samples through multiple rounds of manual
annotation. The results show the significant challenges presented by the ToolBH
benchmark. The current advanced models Gemini-1.5-Pro and GPT-4o only achieve a
total score of 45.3 and 37.0, respectively, on a scale of 100. In this
benchmark, larger model parameters do not guarantee better performance; the
training data and response strategies also play a crucial role in tool-enhanced
LLM scenarios. Our diagnostic analysis indicates that the primary reason for
model errors lies in assessing task solvability. Additionally, open-weight
models suffer from performance drops with verbose replies, whereas proprietary
models excel with longer reasoning.
