mlsanityData to 7 Oct 2026

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

arXiv 2310.01331 ↗First posted 2 Oct 2023cs.HC cs.AIcited by 22

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

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.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 26 of its 117 references that are in the map. First posted on arXiv 2 Oct 2023.

Builds on

26 of 117 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors21 Aug
  2. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate14 Aug
  3. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework1 Aug
  4. 2023Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents5 Jun

Other references · 22

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  3. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  4. 2022Social Simulacra: Creating Populated Prototypes for Social Computing Systemscited by 300
  5. 2020A Survey on Conversational Recommender Systemscited by 142
  6. 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
  7. 2023Understanding the Benefits and Challenges of Using Large Language Model-based Conversational Agents for Mental Well-being Supportcited by 75
  8. 2023CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classescited by 71
  9. 2022Enabling Conversational Interaction with Mobile UI using Large Language Modelscited by 66
  10. 2023Graphologue: Exploring Large Language Model Responses with Interactive Diagramscited by 65
  11. 2023Leveraging Large Language Models in Conversational Recommender Systemscited by 63
  12. 2023ChatGPT: A Meta-Analysis after 2.5 Monthscited by 50
10 more
  1. 2023Sensecape: Enabling Multilevel Exploration and Sensemaking with Large Language Modelscited by 48
  2. 2023Writer-Defined AI Personas for On-Demand Feedback Generationcited by 36
  3. 2020The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systemscited by 34
  4. 2019Tell Me About Yourself: Using an AI-Powered Chatbot to Conduct Conversational Surveys with Open-ended Questionscited by 16
  5. 2023Prompting a Large Language Model to Generate Diverse Motivational Messages: A Comparison with Human-Written Messagescited by 10
  6. 2022Conversational Recommendation: A Grand AI Challengecited by 9
  7. 2023Investigating Conversational Search Behavior For Domain Explorationcited by 9
  8. 2022Crystalline: Lowering the Cost for Developers to Collect and Organize Information for Decision Makingcited by 7
  9. 2023Inform the uninformed: Improving Online Informed Consent Reading with an AI-Powered Chatbotcited by 5
  10. 2022What-if Analysis for Business Professionals: Current Practices and Future Opportunitiescited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 6; 2025: 5; 2026: 8.

22 papers.

Sort:

By area:

  1. 2023Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectivescited by 253
  2. 2024From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agentscited by 76
  3. 2024AI-Augmented Brainwriting: Investigating the use of LLMs in group ideationcited by 43
  4. 2024REAPER: Reasoning based Retrieval Planning for Complex RAG Systemscited by 16
  5. 2024Multi-Agents are Social Groups: Investigating Social Influence of Multiple Agents in Human-Agent Interactionscited by 16
  6. 2025Optimal-Agent-Selection: State-Aware Routing Framework for Efficient Multi-Agent Collaborationcited by 6
  7. 2023GestureGPT: Toward Zero-Shot Free-Form Hand Gesture Understanding with Large Language Model Agentscited by 4
  8. 2024Conversational Agents as Catalysts for Critical Thinking: Challenging Design Fixation in Group Designcited by 4
  9. 2025Conversational Agents as Catalysts for Critical Thinking: Challenging Social Influence in Group Decision-makingcited by 4
  10. 2025Constella: Supporting Storywriters' Interconnected Character Creation through LLM-based Multi-Agentscited by 4
  11. 2024AI-Enhanced Sensemaking: Exploring the Design of a Generative AI-Based Assistant to Support Genetic Professionalscited by 3
  12. 2025Carbon and Silicon, Coexist or Compete? A Survey on Human-AI Interactions in Agent-based Modeling and Simulationcited by 3
10 more
  1. 2026Multi-Agent Debate Strategies: Survey, Taxonomy, and Challengescited by 3
  2. 2023The Use of Multiple Conversational Agent Interlocutors in Learningcited by 2
  3. 2026Social Dynamics as Critical Vulnerabilities that Undermine Objective Decision-Making in LLM Collectivescited by 2
  4. 2026Multi-Agent Systems Shape Social Norms for Prosocial Behavior Changecited by 1
  5. 2026More Isn't Always Better: Balancing Decision Accuracy and Conformity Pressures in Multi-AI Advicecited by 1
  6. 2025SimViews: An Interactive Multi-Agent System Simulating Visitor-to-Visitor Conversational Patterns to Present Diverse Perspectives of Artifacts in Virtual Museumscited by 0
  7. 2026Exploring Learners' Expectations and Engagement When Collaborating with Constructively Controversial Peer Agentscited by 0
  8. 2026When Nobody Around Is Real: Exploring Public Opinions and User Experiences On the Multi-Agent AI Social Platformcited by 0
  9. 2026Decisive: Guiding User Decisions with Optimal Preference Elicitation from Unstructured Documentscited by 0
  10. 2026DocuTeam: Mixed-Initiative Multi-Agent Discussions around Evolving Documentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Beyond expert users: agents should help users construct preferences, not just elicit them
  2. 2026Characterizing Web Search by Conversational LLM Agents: From Search Decisions and Strategies to Results and Responses
  3. 2025UserBench: An Interactive Gym Environment for User-Centric Agents
  4. 2026AcCoRD: Evaluating User-Agent Collaboration Under Realistic User Preference Dynamics
  5. 2025A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directions
  6. 2026LLM Agents for Deliberative Collaboration: A Study on Joint Decision Making Under Partial Observability
  7. 2026PrefIx: Understand and Adapt to User Preference in Human-Agent Interaction
  8. 2025The Social Laboratory: A Psychometric Framework for Multi-Agent LLM Evaluation