CommunityBench: 跨多样化群体和任务的社区级对齐基准测试
CommunityBench: Benchmarking Community-Level Alignment across Diverse Groups and Tasks
AI总结：
CommunityBench通过四个基于共同身份和共同纽带理论的任务，评估了社区级对齐方法，揭示了现有LLMs在建模社区特定偏好上的局限性，并探索了社区级对齐在促进个体建模中的潜力。
摘要：
大型语言模型（LLMs）的对齐确保模型行为反映人类价值观。现有对齐策略主要遵循两条路径：一种假设存在一个统一的目标价值观集（即一揽子解决方案），另一种则将每个人视为独特个体以定制模型（即个体层面）。然而，假设单一的价值空间会忽视少数群体规范，而定制个体模型则成本过高。认识到人类社会由具有高内部价值一致性的社会集群组成，我们提出社区级对齐作为一种
英文摘要：
Large language models (LLMs) alignment ensures model behaviors reflect human value. Existing alignment strategies primarily follow two paths: one assumes a universal value set for a unified goal (i.e., one-size-fits-all), while the other treats every individual as unique to customize models (i.e., individual-level). However, assuming a monolithic value space marginalizes minority norms, while tailoring individual models is prohibitively expensive. Recognizing that human society is organized into social clusters with high intra-group value alignment, we propose community-level alignment as a "middle ground". Practically, we introduce CommunityBench, the first large-scale benchmark for community-level alignment evaluation, featuring four tasks grounded in Common Identity and Common Bond theory. With CommunityBench, we conduct a comprehensive evaluation of various foundation models on CommunityBench, revealing that current LLMs exhibit limited capacity to model community-specific preferences. Furthermore, we investigate the potential of community-level alignment in facilitating individual modeling, providing a promising direction for scalable and pluralistic alignment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
