Authors: K Zhou, JD Hwang, X Ren, N Dziri
Year: 2025
Published in: Proceedings of the ..., 2025 - aclanthology.org
Institution: Stanford University, University of Southern California, Carnegie Mellon University, Allen Institute for AI
Research Area: Human-LM Reliance, Interaction-Centered Framework, Human-Computer Interaction (HCI)
Discipline: Human-Computer Interaction (HCI), Artificial Intelligence
The study introduces Rel-A.I., an interaction-centered evaluation approach to measure human reliance on LLM responses, revealing that politeness and interaction context significantly influence user reliance.
Methods: Nine user studies were conducted, analyzing user reliance influenced by LLM communication features such as politeness and context through participant interaction experiments.
Key Findings: The degree of human reliance on LLM responses based on communication style (e.g., politeness) and interaction context (e.g., knowledge domain, prior interactions).
Citations: 18
Sample Size: 450