LLM safety evaluations for personal advice must test responses against diverse user vulnerability profiles, since context-blind ratings overestimate safety and realistic prompt context does not fix the problem.
CoRR, abs/2506.11094
2 Pith papers cite this work. Polarity classification is still indexing.
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2025 2representative citing papers
LLMEval-Fair introduces a dynamic, contamination-resistant evaluation framework for LLMs based on a large question bank and validates it via a 30-month study of nearly 60 models showing performance ceilings and hidden contamination issues.
citing papers explorer
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Safe for Whom? Rethinking How We Evaluate the Safety of LLMs for Real Users
LLM safety evaluations for personal advice must test responses against diverse user vulnerability profiles, since context-blind ratings overestimate safety and realistic prompt context does not fix the problem.
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LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models
LLMEval-Fair introduces a dynamic, contamination-resistant evaluation framework for LLMs based on a large question bank and validates it via a 30-month study of nearly 60 models showing performance ceilings and hidden contamination issues.