COMMUNITY-CROSS-INSTRUCT：用于将大语言模型与在线社区对齐的无监督指令生成
COMMUNITY-CROSS-INSTRUCT: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities
AI总结：
本文提出无监督框架 Community-Cross-Instruct，通过自动生成指令-输出对微调大语言模型，以低成本、可扩展地表征和调查在线社区的信念。
摘要：
社会科学家使用调查来探查人群的意见和信念，但这些方法缓慢、昂贵且容易产生偏差。大语言模型（LLMs）的最新进展使得能够创建人群的计算表征或“数字孪生”，生成模仿该人群语言、风格和态度的人类式响应。我们提出 Community-Cross-Instruct，这是一个无监督框架，用于将 LLMs 与在线社区对齐以获取其信念。给定一个社区在线讨论的语料库，Community-Cross-Instruct 通过一个先进的 LLM 自动生成指令-输出对，以（1）微调一个基础 LLM 来忠实地表征该社区，以及（2）评估微调后模型与该社区的对齐程度。我们展示了该方法在准确表征 Reddit 上的政治和饮食社区方面的实用性。与先前需要人工编写指令的方法不同，Community-Cross-Instruct 以完全无监督的方式生成指令，增强了跨领域的可扩展性和泛化能力。这项工作使得对多样化在线社区进行成本效益高且自动化的调查成为可能。
英文摘要：
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 creating 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 cost-effective and automated surveying of diverse online communities.
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。
