New PluginEval benchmark improves LLM tool-routing evaluation

Researchers have introduced PluginEval, a new diagnostic benchmark designed to evaluate the tool-routing capabilities of large-language models (LLMs) more effectively. This benchmark addresses limitations in existing methods by ensuring better representation of rare scenarios, incorporating adversarial hard negatives, and validating LLM judgments through actual API execution. PluginEval decomposes plugins by capability and intent, generating queries at various difficulty levels to create a detailed error profile for each model, moving beyond simple aggregate accuracy. AI

IMPACT Enhances the evaluation of LLM agents, potentially leading to more reliable autonomous systems.

RANK_REASON The cluster describes a new benchmark for evaluating LLM capabilities, presented in an academic paper. [lever_c_demoted from research: ic=1 ai=1.0]

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• application programming interface
• arXiv
• Hugging Face
• large-language models
• PluginEval

• paper
• product

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