ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi-Agent Conversational Interactions
From deciding on a PhD program to buying a new camera, unfamiliar decisions--decisions without domain knowledge--are frequent and significant. The complexity and uncertainty of such decisions demand unique approaches to information seeking, understanding, and decision-making. Our formative study highlights that users want to start by discovering broad and relevant domain information evenly and simultaneously, quickly address emerging inquiries, and gain personalized standards to assess information found. We present ChoiceMates, an interactive multi-agent system designed to address these needs by enabling users to engage with a dynamic set of LLM agents each presenting a unique experience in the domain. Unlike existing multi-agent systems that automate tasks with agents, the user orchestrates agents to assist their decision-making process. Our user evaluation (n=12) shows that ChoiceMates enables a more confident, satisfactory decision-making with better situation understanding than web search, and higher decision quality and confidence than a commercial multi-agent framework. This work provides insights into designing a more controllable and collaborative multi-agent system.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingManagementSimilar Papers 제목 키워드 기반
JEF-Hinter: Leveraging Offline Knowledge for Improving Web Agents Adaptation
Large language model (LLM) agents perform well in sequential decision-making tasks, but improving them on unfamiliar domains often requires costly online interactions or fine-tuning on large expert datasets. These strate…
Understanding the Dependence of Perception Model Competency on Regions in an Image
While deep neural network (DNN)-based perception models are useful for many applications, these models are black boxes and their outputs are not yet well understood. To confidently enable a real-world, decision-making sy…
Decision MakingImage CroppingEvaluating Trustworthiness of Online News Publishers via Article Classification
The proliferation of low-quality online information in today's era has underscored the need for robust and automatic mechanisms to evaluate the trustworthiness of online news publishers. In this paper, we analyse the tru…
ArticlesClassificationSelenite: Scaffolding Online Sensemaking with Comprehensive Overviews Elicited from Large Language Models
Sensemaking in unfamiliar domains can be challenging, demanding considerable user effort to compare different options with respect to various criteria. Prior research and our formative study found that people would benef…
Decision MakingNavigateOn the Visualisation of Argumentation Graphs to Support Text Interpretation
The recent evolution in Natural Language Processing (NLP) methods, in particular in the field of argumentation mining, has the potential to transform the way we interact with text, supporting the interpretation and analy…