Beyond Prompts: Learning from Human Communication for Enhanced AI Intent Alignment
May 09, 2024 Β· Declared Dead Β· π arXiv.org
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Authors
Yoonsu Kim, Kihoon Son, Seoyoung Kim, Juho Kim
arXiv ID
2405.05678
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.CL
Citations
7
Venue
arXiv.org
Last Checked
4 months ago
Abstract
AI intent alignment, ensuring that AI produces outcomes as intended by users, is a critical challenge in human-AI interaction. The emergence of generative AI, including LLMs, has intensified the significance of this problem, as interactions increasingly involve users specifying desired results for AI systems. In order to support better AI intent alignment, we aim to explore human strategies for intent specification in human-human communication. By studying and comparing human-human and human-LLM communication, we identify key strategies that can be applied to the design of AI systems that are more effective at understanding and aligning with user intent. This study aims to advance toward a human-centered AI system by bringing together human communication strategies for the design of AI systems.
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