Minh Duc Chu, Zihao He, Rebecca Dorn, Kristina Lerman. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

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 the creation of 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 a 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 diet 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 costeffective and automated surveying of diverse online communities 1 .

Jaspreet Ranjit, Brihi Joshi, Rebecca Dorn, Laura Petry, Olga Koumoundouros, Jayne Bottarini, Peichen Liu, Eric Rice, Swabha Swayamdipta. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.