Words Like Knives: Backstory-Personalized Modeling and Detection of Violent Communication
Journal:
arXiv
Published Date:
May 27, 2025
Abstract
Conversational breakdowns in close relationships are deeply shaped by
personal histories and emotional context, yet most NLP research treats conflict
detection as a general task, overlooking the relational dynamics that influence
how messages are perceived. In this work, we leverage nonviolent communication
(NVC) theory to evaluate LLMs in detecting conversational breakdowns and
assessing how relationship backstory influences both human and model perception
of conflicts. Given the sensitivity and scarcity of real-world datasets
featuring conflict between familiar social partners with rich personal
backstories, we contribute the PersonaConflicts Corpus, a dataset of N=5,772
naturalistic simulated dialogues spanning diverse conflict scenarios between
friends, family members, and romantic partners. Through a controlled human
study, we annotate a subset of dialogues and obtain fine-grained labels of
communication breakdown types on individual turns, and assess the impact of
backstory on human and model perception of conflict in conversation. We find
that the polarity of relationship backstories significantly shifted human
perception of communication breakdowns and impressions of the social partners,
yet models struggle to meaningfully leverage those backstories in the detection
task. Additionally, we find that models consistently overestimate how
positively a message will make a listener feel. Our findings underscore the
critical role of personalization to relationship contexts in enabling LLMs to
serve as effective mediators in human communication for authentic connection.
