Unfamiliar decisions -- decisions where people lack adequate domain knowledge or expertise -- specifically increase the complexity and uncertainty of the process of searching for, understanding, and making decisions with online information. Through our formative study (n=14), we observed users' challenges in accessing diverse perspectives, identifying relevant information, and deciding the right moment to make the final decision. We present ChoiceMates, a system that enables conversations with a dynamic set of LLM-powered agents for a holistic domain understanding and efficient discovery and management of information to make decisions. Agents, as opinionated personas, flexibly join the conversation, not only providing responses but also conversing among themselves to elicit each agent's preferences. Our between-subjects study (n=36) comparing ChoiceMates to conventional web search and single-agent showed that ChoiceMates was more helpful in discovering, diving deeper, and managing information compared to Web with higher confidence. We also describe how participants utilized multi-agent conversations in their decision-making process.

翻译：陌生决策——即人们在缺乏领域知识或专业知识时做出的决策——会显著增加搜索、理解在线信息并据此决策这一过程的复杂性和不确定性。通过形成性研究（n=14），我们观察到用户在获取多元视角、识别相关信息以及确定最终决策最佳时机方面面临的挑战。本文提出ChoiceMates系统，该系统支持用户与一组动态配置的大语言模型智能体进行对话，以实现对决策领域的整体理解，并高效发现和管理决策所需信息。这些智能体以个性化观点持有者的身份灵活参与对话，不仅提供回应，还会相互交流以呈现各自偏好。我们开展了受试者间实验（n=36），将ChoiceMates与传统网络搜索及单一智能体系统进行对比，结果表明：相比于传统网络搜索，ChoiceMates在信息发现、深度探索和管理方面更具帮助性，且用户信心更高。我们还描述了参与者如何在决策过程中运用多智能体对话。