Position: A Roadmap to Pluralistic Alignment

Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell L Gordon, Niloofar Mireshghallah, Christopher Michael Rytting, Andre Ye, Liwei Jiang, Ximing Lu, Nouha Dziri, Tim Althoff, Yejin Choi

Abstract: With increased power and prevalence of AI systems, it is ever more critical that AI systems are designed to serve *all*, i.e., people with diverse values and perspectives. However, aligning models to serve *pluralistic* human values remains an open research question. In this piece, we propose a roadmap to pluralistic alignment, specifically using large language models as a test bed. We identify and formalize three possible ways to define and operationalize pluralism in AI systems: 1) *Overton pluralistic* models that present a spectrum of reasonable responses; 2) *Steerably pluralistic* models that can steer to reflect certain perspectives; and 3) *Distributionally pluralistic* models that are well-calibrated to a given population in distribution. We also formalize and discuss three possible classes of *pluralistic benchmarks*: 1) *Multi-objective* benchmarks, 2) *Trade-off steerable* benchmarks that incentivize models to steer to arbitrary trade-offs, and 3) *Jury-pluralistic* benchmarks that explicitly model diverse human ratings. We use this framework to argue that current alignment techniques may be fundamentally limited for pluralistic AI; indeed, we highlight empirical evidence, both from our own experiments and from other work, that standard alignment procedures might *reduce* distributional pluralism in models, motivating the need for further research on pluralistic alignment.

Submission Number: 7294