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The HUMAINE framework - Unpacking human preference for LLMs

The evaluation of large language models faces significant challenges. Technical benchmarks often lack real-world relevance, while existing human preference evaluations suffer from unrepresentative sampling, superficial assessment depth, and single-metric reductionism.

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Building breakthrough AI faster

Ai2 reduced human data collection from weeks to hours with Prolific, building state-of-the-art multimodal AI models faster without sacrificing quality.

Redefining environmental decision-making

High-quality insights from Prolific participants enabled researchers at Columbia University to explore how people make choices when fighting climate change.

Making AI more democratic with human perspectives

The Collective Intelligence Project used Prolific to bring voices from 70+ countries into the heart of AI governance.