Phare: A Safety Probe for Large Language Models

• URL: http://arxiv.org/abs/2505.11365v1
• Date: Fri, 16 May 2025 15:31:08 GMT
• Title: Phare: A Safety Probe for Large Language Models
• Authors: Pierre Le Jeune, Benoît Malésieux, Weixuan Xiao, Matteo Dora,
• Abstract summary: We introduce Phare, a diagnostic framework to probe and evaluate large language models (LLMs)<n>Our evaluation reveals patterns of systematic vulnerabilities across all safety dimensions, including sycophancy, prompt sensitivity, and stereotype reproduction.<n>Phare provides researchers and practitioners with actionable insights to build more robust, aligned, and trustworthy language systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ensuring the safety of large language models (LLMs) is critical for responsible deployment, yet existing evaluations often prioritize performance over identifying failure modes. We introduce Phare, a multilingual diagnostic framework to probe and evaluate LLM behavior across three critical dimensions: hallucination and reliability, social biases, and harmful content generation. Our evaluation of 17 state-of-the-art LLMs reveals patterns of systematic vulnerabilities across all safety dimensions, including sycophancy, prompt sensitivity, and stereotype reproduction. By highlighting these specific failure modes rather than simply ranking models, Phare provides researchers and practitioners with actionable insights to build more robust, aligned, and trustworthy language systems.

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