  Social scientists use surveys to probe the opinions and beliefs of
populations, but these methods are slow, costly, and prone to biases. Recent
advances in large language models (LLMs) enable creating computational
representations or "digital twins" of populations that generate human-like
responses mimicking the population's language, styles, and attitudes. We
introduce Community-Cross-Instruct, an unsupervised framework for aligning LLMs
to online communities to elicit their beliefs. Given a corpus of a community's
online discussions, Community-Cross-Instruct automatically generates
instruction-output pairs by an advanced LLM to (1) finetune an foundational LLM
to faithfully represent that community, and (2) evaluate the alignment of the
finetuned model to the community. We demonstrate the method's utility in
accurately representing political and fitness communities on Reddit. Unlike
prior methods requiring human-authored instructions, Community-Cross-Instruct
generates instructions in a fully unsupervised manner, enhancing scalability
and generalization across domains. This work enables cost-effective and
automated surveying of diverse online communities.
