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.

通过观察用户在访问多样观点、识别相关信息、决定最佳时机方面遇到的挑战，我们提出了ChoiceMates系统，它通过与一组动态的LLM动力代理进行对话，实现对信息的全面领域理解和高效发现和管理，以作出决策。与传统的网络搜索和单一代理相比，ChoiceMates在发现、深入挖掘和管理信息方面更具帮助性，具有更高的置信度。我们还描述了参与者在决策过程中如何利用多代理对话。

ChoiceMates：用多智能体对话交互支持陌生在线决策