New metric "Expected Harm" challenges LLM safety evaluations

A new research paper proposes a metric called "Expected Harm" to re-evaluate how the safety of large language models is assessed. The authors argue that current methods overemphasize the severity of harmful outputs while underestimating the likelihood of those outputs being realized by real-world actors. Their research indicates that models often refuse low-likelihood threats but remain vulnerable to high-likelihood ones, a miscalibration potentially stemming from supervised fine-tuning data. AI

IMPACT Proposes a new framework for evaluating LLM safety, potentially leading to more robust and reliable AI systems.

RANK_REASON Research paper introducing a new metric for LLM safety evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• Execution Likelihood
• Olmo 3
• supervised fine-tuning
• Yen-Shan Chen

• paper
• safety

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