  The rapid advancement of the Large Language Model (LLM) has created numerous
potentials for integration with conversational assistants (CAs) assisting
people in their daily tasks, particularly due to their extensive flexibility.
However, users' real-world experiences interacting with these assistants remain
unexplored. In this research, we chose cooking, a complex daily task, as a
scenario to investigate people's successful and unsatisfactory experiences
while receiving assistance from an LLM-based CA, Mango Mango. We discovered
that participants value the system's ability to provide extensive information
beyond the recipe, offer customized instructions based on context, and assist
them in dynamically planning the task. However, they expect the system to be
more adaptive to oral conversation and provide more suggestive responses to
keep users actively involved. Recognizing that users began treating our LLM-CA
as a personal assistant or even a partner rather than just a recipe-reading
tool, we propose several design considerations for future development.
