Menta用小模型实现手机端实时心理状态预测，精度超大模型且更省资源。
Menta: A Small Language Model for On-Device Mental Health Prediction
- 基于LoRA微调多任务小模型，融合跨数据集训练策略
- 比最优非微调小模型平均提升15.2%，抑郁与压力识别优于130亿参数大模型
- 可在iPhone 15 Pro Max上实时运行，仅需3GB内存，适合隐私敏感场景
全球数亿人受心理健康问题影响，但早期检测仍受限。尽管大语言模型在心理健康领域展现出潜力，其庞大的体积和计算需求阻碍了实际部署。小语言模型（SLMs）提供了轻量化替代方案，但用于社交媒体心理状态预测的研究仍较少。本文提出Menta，首个专为社交媒体心理状态多任务预测优化的小语言模型。Menta通过基于LoRA的框架，在六项分类任务上联合训练，采用跨数据集策略与平衡准确率导向损失函数。在九个先进小模型基线上评估，相比表现最佳的非微调小模型，平均提升15.2%；在抑郁和压力分类任务上，精度超过130亿参数的大模型，同时模型规模缩小约3.25倍。此外，我们演示了Menta在iPhone 15 Pro Max上的实时端侧部署，仅需约3GB内存。综合对比现有小模型与大模型的基准测试表明，Menta展示了可扩展、隐私保护的心理健康监测潜力。代码已公开：https://hong-labs.github.io/menta-project/
原文摘要 · Abstract (English)
Mental health conditions affect hundreds of millions globally, yet early detection remains limited. While large language models (LLMs) have shown promise in mental health applications, their size and computational demands hinder practical deployment. Small language models (SLMs) offer a lightweight alternative, but their use for social media--based mental health prediction remains largely underexplored. In this study, we introduce Menta, the first optimized SLM fine-tuned specifically for multi-task mental health prediction from social media data. Menta is jointly trained across six classification tasks using a LoRA-based framework, a cross-dataset strategy, and a balanced accuracy--oriented loss. Evaluated against nine state-of-the-art SLM baselines, Menta achieves an average improvement of 15.2\% across tasks covering depression, stress, and suicidality compared with the best-performing non--fine-tuned SLMs. It also achieves higher accuracy on depression and stress classification tasks compared to 13B-parameter LLMs, while being approximately 3.25x smaller. Moreover, we demonstrate real-time, on-device deployment of Menta on an iPhone 15 Pro Max, requiring only approximately 3GB RAM. Supported by a comprehensive benchmark against existing SLMs and LLMs, Menta highlights the potential for scalable, privacy-preserving mental health monitoring. Code is available at: https://hong-labs.github.io/menta-project/
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