用强化学习自动补全心理互助帖缺失信息，提升回应率
Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communities
- 通过强化学习动态识别帖子中缺失的支持要素
- 在4个大模型上使支持信息获取率显著提升
- 适合研究在线心理健康社区与人机交互的学者
在线心理健康社区（OMHCs）提供重要同行与专家支持，但许多帖子因缺少关键支持属性而未获回应。本文提出一种新框架，识别这些信息缺口并提示用户补充内容以提升参与度。为此，构建了REDDME数据集，包含来自心理健康子版块的4,760篇标注帖子，涵盖事件、影响和需求三个核心支持属性的范围与强度。设计了层级化属性分类体系CueTaxo，用于可控问题生成。进一步提出MH-COPILOT系统，融合上下文属性定位、属性强度分类、基于层级分类的问题生成及验证器用于奖励建模。该模型动态评估帖子中支持属性的有无，并生成针对性提示以获取缺失信息。在四个主流语言模型上的实证结果表明，该方法显著提升了属性提取效果与用户参与度。人工评估也证实其在真实在线心理健康社区中的有效性。
原文摘要 · Abstract (English)
Online Mental Health Communities (OMHCs) provide crucial peer and expert support, yet many posts remain unanswered due to missing support attributes that signal the need for help. We present a novel framework that identifies these gaps and prompts users to enrich their posts, thereby improving engagement. To support this, we introduce REDDME, a new dataset of 4,760 posts from mental health subreddits annotated for the span and intensity of three key support attributes: event what happened?, effect what did the user experience?, and requirement what support they need?. Next, we devise a hierarchical taxonomy, CueTaxo, of support attributes for controlled question generation. Further, we propose MH-COPILOT, a reinforcement learning-based system that integrates (a) contextual attribute-span identification, (b) support attribute intensity classification, (c) controlled question generation via a hierarchical taxonomy, and (d) a verifier for reward modeling. Our model dynamically assesses posts for the presence/absence of support attributes, and generates targeted prompts to elicit missing information. Empirical results across four notable language models demonstrate significant improvements in attribute elicitation and user engagement. A human evaluation further validates the model's effectiveness in real-world OMHC settings.
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