Abstract
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.
Abstract (translated)
在情感支持对话中，虽然善意的积极态度有时会误入歧途，导致回应显得轻率、忽视问题或过分乐观，但这种不一致的正面情绪会被视为对情感支持的一种不当表达。我们研究了人类和大型语言模型（LLM）生成回复中的此类现象。 为此，我们在Reddit上收集了一系列具有不同情感强度的真实用户-助手对话，并使用大型语言模型为相同情境生成额外回应。我们将这些对话按强度分为两个层次：轻度层包括关系紧张和一般性建议；重度层则涵盖哀伤和焦虑的对话。这种分类方法使我们能够比较分析在低风险和高风险背景下支持性回应的变化。 我们的研究发现，在高风险背景下，大型语言模型更倾向于通过轻率或忽视问题的态度表现出不切实际的乐观情绪。为了进一步探究这一现象的本质维度，我们在具有强烈与轻微情感反应的数据集上对大型语言模型进行了微调。此外，我们开发了一种弱监督多标签分类器集合（DeBERTa和MentalBERT），该系统在轻度和重度两种类型的担忧中都显示出更准确地检测不一致的正面情绪的能力。 我们的研究结果揭示了超越生成一般积极回应的重要性，并强调需要研究如何平衡情感承认与积极情感的支持措施。这种做法为大型语言模型在在线支持性对话中的情感预期对齐提供了见解，也为开发上下文感知和信任维护的在线交流系统铺平道路。
URL
https://arxiv.org/abs/2509.10184
