A Taxonomy for Human-LLM Interaction Modes: An Initial Exploration
Abstract
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.
Cite
@article{arxiv.2404.00405,
title = {A Taxonomy for Human-LLM Interaction Modes: An Initial Exploration},
author = {Jie Gao and Simret Araya Gebreegziabher and Kenny Tsu Wei Choo and Toby Jia-Jun Li and Simon Tangi Perrault and Thomas W. Malone},
journal= {arXiv preprint arXiv:2404.00405},
year = {2024}
}
Comments
11 pages, 4 figures, 3 tables. Accepted at CHI Late-Breaking Work 2024