如刀之言：基于背景故事个性化的暴力沟通建模与检测
Words Like Knives: Backstory-Personalized Modeling and Detection of Violent Communication
AI总结：
本文基于非暴力沟通理论构建PersonaConflicts Corpus，研究关系背景故事对人类和LLMs冲突检测的影响，发现背景故事显著影响人类感知而模型难以有效利用，并持续高估信息的积极感受，强调个性化对LLMs作为沟通调解者的重要性。
摘要：
亲密关系中的对话破裂深受个人经历和情感语境的影响，然而大多数自然语言处理研究将冲突检测视为一般性任务，忽视了影响信息感知方式的关系动态。在本工作中，我们利用非暴力沟通（NVC）理论评估大语言模型（LLMs）在检测对话破裂方面的能力，并考察关系背景故事如何影响人类和模型对冲突的感知。鉴于现实世界中包含熟悉社交伙伴之间冲突且具有丰富个人背景故事的数据集十分敏感且稀缺，我们构建了PersonaConflicts Corpus，这是一个包含N=5,772段自然主义模拟对话的数据集，涵盖朋友、家庭成员和恋人之间多样化的冲突场景。通过一项受控人类研究，我们对部分对话进行了标注，获得了单个话轮上对话破裂类型的细粒度标签，并评估了背景故事对人类和模型在对话中冲突感知的影响。我们发现，关系背景故事的情感极性显著改变了人类对对话破裂的感知以及对社交伙伴的印象，然而模型在检测任务中难以有效利用这些背景故事。此外，我们发现模型持续高估了一条信息会让听者产生积极感受的程度。我们的研究结果强调了针对关系语境进行个性化对于使大语言模型能够在人类沟通中充当有效调解者、促进真实连接的关键作用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
