New benchmark quantifies LLM API divergence across domains

Researchers have developed a new framework to measure how much different large language models (LLMs) disagree when they try to find and rank external APIs for tasks. Across various API domains and major model families, the study found moderate agreement but significant differences depending on the task type. Structured tasks showed more consistency, while open-ended reasoning tasks led to greater divergence, highlighting a potential safety risk in multi-agent LLM coordination. AI

IMPACT Reveals hidden divergence in LLM coordination, posing a pre-deployment safety risk for multi-agent systems.

RANK_REASON Academic paper introducing a new benchmarking framework for LLM API retrieval and ranking.

Read on arXiv cs.CL →

• Cronbach's alpha
• Jaccard similarity
• Kendall's W
• LLM
• Rank-Biased Overlap
• Sentiment Analysis
• Speech-to-Text
• Weather
• Kendall's tau

• paper
• safety

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