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"Mango Mango, How to Let The Lettuce Dry Without A Spinner?": Exploring User Perceptions of Using An LLM-Based

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Summary

This study explored user experiences with a Large Language Model (LLM)-based conversational assistant (CA) for cooking. Findings highlight the need for more adaptive, engaging interactions, moving beyond simple recipe delivery.

Keywords:
exploratory studylarge language model-based conversational assistantuser study

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Area of Science:

  • Human-Computer Interaction
  • Artificial Intelligence Applications
  • Natural Language Processing

Background:

  • Large Language Models (LLMs) offer significant potential for enhancing conversational assistants (CAs).
  • User experiences with LLM-powered CAs in complex daily tasks are largely unexplored.
  • Cooking presents a suitable domain for investigating user-CA interactions due to its complexity.

Purpose of the Study:

  • To investigate user experiences with a specific LLM-based CA, "Mango Mango", during cooking tasks.
  • To identify successful and unsatisfactory aspects of user interaction with the LLM-CA.
  • To inform future design considerations for LLM-powered conversational assistants.

Main Methods:

  • Qualitative exploration of user experiences with an LLM-based CA during cooking.
  • Analysis of participant interactions and feedback regarding system performance.
  • Identification of user expectations and perceived limitations of the CA.

Main Results:

  • Users valued customized instructions, contextual information, and dynamic task planning support.
  • Participants desired more adaptive oral conversation capabilities from the CA.
  • Users expected more suggestive responses to enhance engagement and involvement.
  • A tendency for users to perceive the LLM-CA as a personal assistant or partner was observed.

Conclusions:

  • LLM-based CAs show promise in assisting complex daily tasks like cooking.
  • Future LLM-CA development should prioritize conversational adaptiveness and user engagement.
  • Five key design considerations are proposed for enhancing future LLM-CA systems.