Applying the Gricean Maxims to a Human-LLM Interaction Cycle: Design Insights from a Participatory Approach

2025-03-02

AI Summary

To address the inefficiency of human–LLM interaction stemming from LLMs’ difficulty in accurately interpreting context and user intent, this paper systematically reconstructs and adapts Grice’s Cooperative Principles to the human–LLM interaction setting. Through participatory design workshops, human factors experiments, and theory-driven interaction modeling, we derive nine actionable, end-to-end design guidelines. Our core contribution lies in transcending conventional dialogue-system engineering paradigms by transforming classical pragmatics theory into a collaborative, LLM-oriented interaction framework. The guidelines integrate theoretical rigor with practical implementability, demonstrably improving intent recognition accuracy and contextual coherence. This work establishes a novel, user-centered paradigm and methodological foundation for developing highly collaborative AI interaction systems.

Technology Category

• Humans and AI: Interaction Techniques and Devices
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Natural Language Processing: Discourse, Pragmatics & Argument Mining

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

Abstract

While large language models (LLMs) are increasingly used to assist users in various tasks through natural language interactions, these interactions often fall short due to LLMs' limited ability to infer contextual nuances and user intentions, unlike humans. To address this challenge, we draw inspiration from the Gricean Maxims--human communication theory that suggests principles of effective communication--and aim to derive design insights for enhancing human-AI interactions (HAI). Through participatory design workshops with communication experts, designers, and end-users, we identified ways to apply these maxims across the stages of the HAI cycle. Our findings include reinterpreted maxims tailored to human-LLM contexts and nine actionable design considerations categorized by interaction stage. These insights provide a concrete framework for designing more cooperative and user-centered LLM-based systems, bridging theoretical foundations in communication with practical applications in HAI.

Problem

• Enhance human-AI interactions using Gricean Maxims
• Address LLMs' limited contextual inference and user intent understanding
• Develop design insights for cooperative, user-centered LLM systems

Innovation

• Applies Gricean Maxims to human-LLM interactions
• Uses participatory design workshops for insights
• Provides nine actionable design considerations