New benchmark RetryBench probes LLM tool use reliability

A new research paper introduces RetryBench, a benchmark designed to evaluate the ability of large language models to distinguish between valid retry permissions and actual recovery outcomes when using tools. The study found that models like Sol, Terra, and Luna exhibited issues with false retry permissions, attempting to retry tool calls even when not explicitly permitted by the contract. The research suggests that reporting retry permission and recovery outcomes together is crucial for understanding and improving the reliability of LLM tool use. AI

IMPACT Highlights a critical area for improving LLM reliability in tool-use scenarios, potentially impacting agent development and safety.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new benchmark for evaluating LLM tool use. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• generative pre-trained transformer
• Hugging Face
• Luna
• RetryBench
• Sol

• paper
• safety

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