Year: 2025
Type: conference-paper
Abstract: AI support of collaborative interactions entails mediating potential misalignment between interlocutor beliefs.Common preference alignment methods like DPO excel in static settings, but struggle in dynamic collaborative tasks where the explicit signals of interlocutor beliefs are sparse and skewed.We propose the Frictional Agent Alignment Framework (FAAF), to generate precise, context-aware "friction" that prompts for deliberation and re-examination of existing evidence.FAAF's two-player objective decouples from data skew: a frictive-state policy identifies belief misalignments, while an intervention policy crafts collaborator-preferred responses.We derive an analytical solution to this objective, enabling training a single policy via a simple supervised loss.Experiments on three benchmarks show FAAF outperforms competitors in producing concise, interpretable friction and in OOD generalization.By aligning LLMs to act as adaptive "thought partners"-not passive responders-FAAF advances scalable, dynamic human-AI collaboration.Our code and data can be found at https: //github.com/csu-signal/FAAF_ACL.
Institution: Colorado State University
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412945373