Core Concepts
This paper presents a taxonomy that categorizes the various interaction modes between humans and large language models (LLMs), aiming to empower users to tackle complex tasks by utilizing LLMs beyond the default conversational prompting paradigm.
Abstract
The paper presents a systematic review of existing literature in HCI venues published since 2021, which led to the identification of four key phases in human-LLM interaction flows: planning, facilitating, iterating, and testing. Additionally, the research introduces a detailed, structured taxonomy that encapsulates four primary interaction modes between humans and LLMs:
Standard Prompting:
Mode 1.1 Text-based Conversational Prompting
Mode 1.2 Text-based Conversational Prompting with Reasoning
User Interface (UI):
Mode 2.1 UI for Structured Prompts Input
Mode 2.2 UI for Varying Output
Mode 2.3 UI for Iteration of Interaction
Mode 2.4 UI for Testing of Interaction
Mode 2.5 UI for Reasoning
Context-based:
Mode 3.1 Explicit Context
Mode 3.2 Implicit Context
Agent Facilitator:
Mode 4.1 Team Process Facilitating
Mode 4.2 Capability-aware Task Delegation
The taxonomy provides a valuable tool for systematically understanding and analyzing the evolving landscape of human-LLM interaction and collaboration, guiding the design of human engagement with LLMs in increasingly complex and nuanced ways.