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Leveraging Large Language Models to Power Chatbots for Collecting User Self-Reported Data

Published: 26 April 2024 Publication History

Abstract

Large language models (LLMs) provide a new way to build chatbots by accepting natural language prompts. Yet, it is unclear how to design prompts to power chatbots to carry on naturalistic conversations while pursuing a given goal such as collecting self-report data from users. We explore what design factors of prompts can help steer chatbots to talk naturally and collect data reliably. To this aim, we formulated four prompt designs with different structures and personas. Through an online study (N = 48) where participants conversed with chatbots driven by different designs of prompts, we assessed how prompt designs and conversation topics affected the conversation flows and users' perceptions of chatbots. Our chatbots covered 79% of the desired information slots during conversations, and the designs of prompts and topics significantly influenced the conversation flows and the data collection performance. We discuss the opportunities and challenges of building chatbots with LLMs.

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cover image Proceedings of the ACM on Human-Computer Interaction
Proceedings of the ACM on Human-Computer Interaction  Volume 8, Issue CSCW1
CSCW
April 2024
6294 pages
EISSN:2573-0142
DOI:10.1145/3661497
Issue’s Table of Contents
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Published: 26 April 2024
Published in PACMHCI Volume 8, Issue CSCW1

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  1. chatbots
  2. conversational agents
  3. dialogue acts
  4. large language models

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  • (2024)MindfulDiary: Harnessing Large Language Model to Support Psychiatric Patients' JournalingProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642937(1-20)Online publication date: 11-May-2024
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