New benchmark ToolRobustBench diagnoses LLM tool-calling failures

Researchers have introduced ToolRobustBench, a new benchmark designed to evaluate and diagnose failures in tool-calling agents, which are LLM systems that use external tools to complete tasks. The benchmark systematically introduces perturbations across four categories—tool-interface, user-intent, tool-output/observation, and runtime-environment—to pinpoint specific failure points within the tool-use pipeline. Experiments involving multiple LLMs and tools revealed significant robustness degradation, with tool-output/observation perturbations identified as a major bottleneck, highlighting the need for more robust tool-calling capabilities. AI

IMPACT Enhances evaluation of LLM agent robustness, guiding development towards more reliable tool integration.

RANK_REASON The cluster contains a research paper introducing a new benchmark for evaluating LLM tool-calling capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• LLMs
• runtime-environment perturbations
• tool calling
• tool-interface perturbations
• tool-output/observation perturbations
• ToolRobustBench
• tool-use pipeline
• user-intent perturbations

• paper
• product

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