# A Map of Exploring Human Interaction patterns with LLM: Insights into Collaboration and Creativity

arXiv: https://arxiv.org/abs/2404.04570 · HTML: https://mlsanity.com/paper/2404.04570/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2024.

## Built on by (most cited first, 3 of 3)

- [A Survey on Human-Centric LLMs](https://mlsanity.com/paper/2411.14491/) (2024)
- [Human-AI collaboration or obedient and often clueless AI in instruct, serve, repeat dynamics?](https://mlsanity.com/paper/2508.10919/) (2025)
- [ASSEMCAD: Production-Ready CAD Assembly Generation from Natural Language](https://mlsanity.com/paper/2607.05123/) (2026)

## Abstract (arXiv)

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.