用大模型集成与动态标注提升心理健康变化的识别与总结能力
CUNY at CLPsych 2026: A Pipeline Approach to Classification and Summarization of Mental Health Changes
- 三模型集成+多数投票，精准识别社交媒体中的心理状态
- 基于状态预测结果训练分类器，定位情绪变化关键时间点
- 通过上游预测结果增强标注，显著提升情绪动态总结效果
我们针对CLPsych 2026共享任务中通过社交媒体时间线动态捕捉与刻画心理健康变化的问题提出解决方案。为推断帖子中的主导自我状态（任务1.1和1.2），采用三种开源大语言模型的上下文学习并进行多数投票集成。为预测时间线中变化时刻（任务2），在任务1.1预测结果基础上训练监督分类器。为总结情绪动态模式及其随时间演变特征（任务3.1），对上游系统（任务1.1、1.2、2）预测的示例标签进行增强，相比零样本和未增强的上下文学习基线取得性能提升。提交方案在任务1.1排名第一，任务1.2和任务2均第四，任务3.1排名第三。
原文摘要 · Abstract (English)
We describe our submission to the CLPsych~2026 Shared Task on capturing and characterizing mental health changes through social media timeline dynamics. To infer the dominant self-states in posts (Tasks 1.1 and 1.2), we ensemble in-context learning of three open-weight large language models using majority voting. For predicting moments of change in a timeline (Task~2), we train supervised classifiers on features derived from Task~1.1 predictions. To summarize the patterns of mood dynamics and their progression over time within a timeline (Task 3.1), we augment in-context example labels predicted by upstream systems (Tasks 1.1, 1.2, and 2), yielding performance gains over zero-shot and unaugmented in-context learning baselines. Our submission ranked first on Task~1.1, fourth on Task~1.2, fourth on Task~2, and third on Task~3.1.\footnote{The source code for the experiments is available at https://github.com/amirzia/clpsych26-cuny
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。