A Persona-Based Evaluation Framework for Pluralistic Alignment in Generative AI
Moving beyond monolithic benchmarks through simulated cognitive personas and dynamic state-space monitoring.
Current alignment paradigms for generative artificial intelligence rely predominantly on monolithic benchmarking frameworks that reduce the plurality of human judgment to aggregated statistical baselines, thereby obscuring cultural, demographic, and contextual variability in eval...
ArXiv CS.AI
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This research highlights the critical transition from one-size-fits-all benchmarking to dynamic, persona-based evaluation to better capture diverse human perspectives. It warns that current alignment methods fail to account for the cognitive drift that occurs when AI models attempt to simulate complex human viewpoints over time.
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