  The outstanding performance capabilities of large language model have driven
the evolution of current AI system interaction patterns. This has led to
considerable discussion within the Human-AI Interaction (HAII) community.
Numerous studies explore this interaction from technical, design, and empirical
perspectives. However, the majority of current literature reviews concentrate
on interactions across the wider spectrum of AI, with limited attention given
to the specific realm of interaction with LLM. We searched for articles on
human interaction with LLM, selecting 110 relevant publications meeting
consensus definition of Human-AI interaction. Subsequently, we developed a
comprehensive Mapping Procedure, structured in five distinct stages, to
systematically analyze and categorize the collected publications. Applying this
methodical approach, we meticulously mapped the chosen studies, culminating in
a detailed and insightful representation of the research landscape. Overall,
our review presents an novel approach, introducing a distinctive mapping
method, specifically tailored to evaluate human-LLM interaction patterns. We
conducted a comprehensive analysis of the current research in related fields,
employing clustering techniques for categorization, which enabled us to clearly
delineate the status and challenges prevalent in each identified area.
