CommunityBench: Benchmarking Community-Level Alignment across Diverse Groups and Tasks

Jiayu Lin; Zhongyu Wei

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arXiv · 2601.13669

CommunityBench: Benchmarking Community-Level Alignment across Diverse Groups and Tasks

Jiayu Lin·Zhongyu Wei

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Abstract

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.

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Jiayu Lin, Zhongyu Wei. 2026-01-20. CommunityBench: Benchmarking Community-Level Alignment across Diverse Groups and Tasks. https://arxiv.org/abs/2601.13669

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