用4位量化模型和句子分块法，提升心理健康文本中自我状态分类准确率。
A Baseline for Self-state Identification and Classification in Mental Health Data: CLPsych 2025 Task
- 先提取相关句子再二分类，匹配人类标注粒度。
- 测试召回率达0.579，排名第三，优于长跨度文本识别方法。
- 适合做心理状态分析的轻量级基准系统，可快速部署。
我们为CLPsych 2025 A.1任务提出了一个基准系统：基于Reddit的心理健康数据进行自我状态分类。采用4比特量化Gemma 2 9B模型与少样本学习，通过两步流程——先识别表达自我状态的句子，再判断其为适应性或非适应性状态——实现分类。该系统在测试阶段召回率为0.579，排名十四支参赛系统中的第三名。相比依赖大模型独立标注可变长度片段的方法，本方案表现更优。我们认为性能提升源于句子分块设计：一方面，句子粒度与人工标注一致；另一方面，将复杂任务简化为二分类问题，降低模型负担。
原文摘要 · Abstract (English)
We present a baseline for the CLPsych 2025 A.1 task: classifying self-states in mental health data taken from Reddit. We use few-shot learning with a 4-bit quantized Gemma 2 9B model and a data preprocessing step which first identifies relevant sentences indicating self-state evidence, and then performs a binary classification to determine whether the sentence is evidence of an adaptive or maladaptive self-state. This system outperforms our other method which relies on an LLM to highlight spans of variable length independently. We attribute the performance of our model to the benefits of this sentence chunking step for two reasons: partitioning posts into sentences 1) broadly matches the granularity at which self-states were human-annotated and 2) simplifies the task for our language model to a binary classification problem. Our system places third out of fourteen systems submitted for Task A.1, achieving a test-time recall of 0.579.
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