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Unfamiliar decisions -- decisions where people lack adequate domain knowledgeor expertise -- specifically increase the complexity and uncertainty of theprocess of searching for, understanding, and making decisions with onlineinformation. Through our formative study (n=14), we observed users' challengesin accessing diverse perspectives, identifying relevant information, anddeciding the right moment to make the final decision. We present ChoiceMates, asystem that enables conversations with a dynamic set of LLM-powered agents fora holistic domain understanding and efficient discovery and management ofinformation to make decisions. Agents, as opinionated personas, flexibly jointhe conversation, not only providing responses but also conversing amongthemselves to elicit each agent's preferences. Our between-subjects study(n=36) comparing ChoiceMates to conventional web search and single-agent showedthat ChoiceMates was more helpful in discovering, diving deeper, and managinginformation compared to Web with higher confidence. We also describe howparticipants utilized multi-agent conversations in their decision-makingprocess.

ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi-Agent Conversational Interactions