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The rapid advancement of the Large Language Model (LLM) has created numerouspotentials for integration with conversational assistants (CAs) assistingpeople in their daily tasks, particularly due to their extensive flexibility.However, users' real-world experiences interacting with these assistants remainunexplored. In this research, we chose cooking, a complex daily task, as ascenario to investigate people's successful and unsatisfactory experienceswhile receiving assistance from an LLM-based CA, Mango Mango. We discoveredthat participants value the system's ability to provide extensive informationbeyond the recipe, offer customized instructions based on context, and assistthem in dynamically planning the task. However, they expect the system to bemore adaptive to oral conversation and provide more suggestive responses tokeep users actively involved. Recognizing that users began treating our LLM-CAas a personal assistant or even a partner rather than just a recipe-readingtool, we propose several design considerations for future development.

"Mango Mango, How to Let The Lettuce Dry Without A Spinner?'': Exploring  User Perceptions of Using An LLM-Based Conversational Assistant Toward  Cooking Partner