The paper "Position: A Roadmap to Pluralistic Alignment" by Taylor Sorensen et al. discusses the importance of designing AI systems that can serve diverse values and perspectives, a concept known as pluralism. The authors propose a roadmap for pluralistic alignment, focusing on large language models (LLMs) as a testbed. They identify three ways to define and operationalize pluralism: Overton pluralistic models, Steerably pluralistic models, and Distributionally pluralistic models. Each type of model is characterized by its ability to present a spectrum of reasonable responses, steer to reflect specific perspectives, and match the distribution of a given population, respectively. The paper also introduces three types of pluralistic benchmarks: multi-objective benchmarks, trade-off steerable benchmarks, and jury-pluralistic benchmarks. These benchmarks are designed to measure the alignment of models with multiple objectives, allow models to be steered to different trade-offs, and explicitly model diverse human ratings, respectively. The authors argue that current alignment techniques may reduce distributional pluralism in models, highlighting empirical evidence from their own experiments and other studies. They advocate for further research into pluralistic evaluations and alignment procedures to address these limitations. Key arguments for pluralism in AI systems include customization, technical benefits, enabling generalist systems, and reflecting human diversity. The paper provides detailed definitions, motivations, applications, limitations, and alignment procedures for each form of pluralism and benchmark. It concludes with recommendations for future research and discussions on the broader implications of pluralism in AI systems.The paper "Position: A Roadmap to Pluralistic Alignment" by Taylor Sorensen et al. discusses the importance of designing AI systems that can serve diverse values and perspectives, a concept known as pluralism. The authors propose a roadmap for pluralistic alignment, focusing on large language models (LLMs) as a testbed. They identify three ways to define and operationalize pluralism: Overton pluralistic models, Steerably pluralistic models, and Distributionally pluralistic models. Each type of model is characterized by its ability to present a spectrum of reasonable responses, steer to reflect specific perspectives, and match the distribution of a given population, respectively. The paper also introduces three types of pluralistic benchmarks: multi-objective benchmarks, trade-off steerable benchmarks, and jury-pluralistic benchmarks. These benchmarks are designed to measure the alignment of models with multiple objectives, allow models to be steered to different trade-offs, and explicitly model diverse human ratings, respectively. The authors argue that current alignment techniques may reduce distributional pluralism in models, highlighting empirical evidence from their own experiments and other studies. They advocate for further research into pluralistic evaluations and alignment procedures to address these limitations. Key arguments for pluralism in AI systems include customization, technical benefits, enabling generalist systems, and reflecting human diversity. The paper provides detailed definitions, motivations, applications, limitations, and alignment procedures for each form of pluralism and benchmark. It concludes with recommendations for future research and discussions on the broader implications of pluralism in AI systems.