不一致的积极性：当不准确的积极性削弱在线支持性对话
Incongruent Positivity: When Miscalibrated Positivity Undermines Online Supportive Conversations
AI总结：
研究探讨了在高压力情境中，LLM生成的不一致积极性如何导致消极回应，提出通过微调模型和开发多标签分类器来提升支持性对话的质量。
摘要：
在情感支持对话中，本意良好的积极性有时会失效，导致回应显得轻视、最小化或不切实际。本文研究了这种不一致的积极性现象，分析了人类和LLM生成的回应。通过收集Reddit上的真实用户-助手对话，并生成额外回应，将对话按强度分为轻度和重度两类。分析发现，LLM在高压力情境中更倾向于产生不切实际的积极性，通过微调模型和开发弱监督多标签分类器，提高了对不一致积极性的检测能力。研究强调需超越生成通用积极回应，转而研究平衡积极情绪与情感认可的措施，为构建情境感知且信任保留的在线对话系统提供思路。
英文摘要：
In emotionally supportive conversations, well-intended positivity can sometimes misfire, leading to responses that feel dismissive, minimizing, or unrealistically optimistic. We examine this phenomenon of incongruent positivity as miscalibrated expressions of positive support in both human and LLM generated responses. To this end, we collected real user-assistant dialogues from Reddit across a range of emotional intensities and generated additional responses using large language models for the same context. We categorize these conversations by intensity into two levels: Mild, which covers relationship tension and general advice, and Severe, which covers grief and anxiety conversations. This level of categorization enables a comparative analysis of how supportive responses vary across lower and higher stakes contexts. Our analysis reveals that LLMs are more prone to unrealistic positivity through dismissive and minimizing tone, particularly in high-stakes contexts. To further study the underlying dimensions of this phenomenon, we finetune LLMs on datasets with strong and weak emotional reactions. Moreover, we developed a weakly supervised multilabel classifier ensemble (DeBERTa and MentalBERT) that shows improved detection of incongruent positivity types across two sorts of concerns (Mild and Severe). Our findings shed light on the need to move beyond merely generating generic positive responses and instead study the congruent support measures to balance positive affect with emotional acknowledgment. This approach offers insights into aligning large language models with affective expectations in the online supportive dialogue, paving the way toward context-aware and trust preserving online conversation systems.
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。
