Can Gricean norms improve LLM agent collaboration?
Gricean Norms as a Basis for Effective Collaboration
March 19, 2025
https://arxiv.org/pdf/2503.14484This paper explores how to improve human-AI collaboration by teaching AI agents to better understand and respond to unclear natural language instructions. It proposes a framework that incorporates Gricean norms (rules of conversation like be truthful, relevant, and clear) into LLM-based agents called "Lamoids."
Key points for LLM-based multi-agent systems:
- Gricean Norms improve instruction interpretation: Integrating conversational rules helps LLMs discern intent from ambiguous, incomplete, or irrelevant instructions.
- Inference norm handles unclear instructions: When instructions violate conversational norms, the agent seeks clarification or infers the implied meaning.
- Cognitive frameworks enhance context awareness: Combining Gricean norms with common ground, relevance theory, and theory of mind improves the agent's understanding of the collaborative context.
- Few-shot Chain-of-Thought prompting is key: Providing examples of norm-aligned responses improves LLM performance.
- Limitations of LLMs in spatial reasoning: Challenges remain in applying LLMs to tasks requiring spatial understanding, highlighting the need for specialized tools or hybrid models.
- Prompt engineering complexity: Extensive manual prompt engineering is a current bottleneck.