Phare: A Safety Probe for Large Language Models

Hacker News

We've just published a benchmark and accompanying paper on arXiv that challenges conventional leaderboard-driven LLM evaluation. Phare focuses on factual reliability, prompt sensitivity, multilingual support, and how models handle false premises like issues that actually matter when you're building serious applications. Some insights: - Preference scores ≠ factual correctness. - Framing effects can cause models to miss obvious falsehoods. - Safety metrics like sycophancy and stereotype reproduction show surprising results across popular models. Would love feedback from the community.

Visit product View on Hacker NewsMay 21, 2025

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