Pluralistic alignment is the problem of teaching AI systems to serve populations whose preferences are not uniform—and may even conflict. The abstract for this arXiv paper frames that challenge and points to anonymized crowd feedback as a way to learn from a broad range of users. It also signals that current methods fall short, though the excerpt cuts off before describing the proposed approach.
Because the available text is only the opening of the abstract, the article cannot summarize the paper's method, experiments, or conclusions. What is clear is the motivation: AI systems should be steerable and personalized, not optimized for a single average preference.