# A Taxonomy for Human-LLM Interaction Modes: An Initial Exploration

arXiv: https://arxiv.org/abs/2404.00405 · HTML: https://mlsanity.com/paper/2404.00405/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2024.

## Built on by (most cited first, 30 of 38)

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- [Exploring Human-LLM Conversations: Mental Models and the Originator of Toxicity](https://mlsanity.com/paper/2407.05977/) (2024)
- [How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond](https://mlsanity.com/paper/2501.05714/) (2025)
- [Unraveling Human-AI Teaming: A Review and Outlook](https://mlsanity.com/paper/2504.05755/) (2025)
- [Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents](https://mlsanity.com/paper/2602.01405/) (2026)
- [Carbon and Silicon, Coexist or Compete? A Survey on Human-AI Interactions in Agent-based Modeling and Simulation](https://mlsanity.com/paper/2502.18145/) (2025)
- [Show or Tell? Modeling the evolution of request-making in Human-LLM conversations](https://mlsanity.com/paper/2508.01213/) (2025)
- [Assessing the Impact of Conspiracy Theories Using Large Language Models](https://mlsanity.com/paper/2412.07019/) (2024)
- [Personality Matters: User Traits Predict LLM Preferences in Multi-Turn Collaborative Tasks](https://mlsanity.com/paper/2508.21628/) (2025)
- [A Case Study Investigating the Role of Generative AI in Quality Evaluations of Epics in Agile Software Development](https://mlsanity.com/paper/2505.07664/) (2025)
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- [Priming, Path-dependence, and Plasticity: Understanding the molding of user-LLM interaction and its implications from (many) chat logs in the wild](https://mlsanity.com/paper/2605.05767/) (2026)
- [DeCoDe: Defer-and-Complement Decision-Making via Decoupled Concept Bottleneck Models](https://mlsanity.com/paper/2505.19220/) (2025)

## Abstract (arXiv)

With ChatGPT's release, conversational prompting has become the most popular form of human-LLM interaction. However, its effectiveness is limited for more complex tasks involving reasoning, creativity, and iteration. Through a systematic analysis of HCI papers published since 2021, we identified four key phases in the human-LLM interaction flow - planning, facilitating, iterating, and testing - to precisely understand the dynamics of this process. Additionally, we have developed a taxonomy of four primary interaction modes: Mode 1: Standard Prompting, Mode 2: User Interface, Mode 3: Context-based, and Mode 4: Agent Facilitator. This taxonomy was further enriched using the "5W1H" guideline method, which involved a detailed examination of definitions, participant roles (Who), the phases that happened (When), human objectives and LLM abilities (What), and the mechanics of each interaction mode (How). We anticipate this taxonomy will contribute to the future design and evaluation of human-LLM interaction.