Depression is a pressing global public health issue, yet publicly available Chinese-language resources for depression risk detection remain scarce and largely focus on binary classification. To address this limitation, we release CNSocialDepress, a benchmark dataset for depression risk detection on Chinese social media. The dataset contains 44,178 posts from 233 users; psychological experts annotated 10,306 depression-related segments. CNSocialDepress provides binary risk labels along with structured, multidimensional psychological attributes, enabling interpretable and fine-grained analyses of depressive signals. Experimental results demonstrate the dataset’s utility across a range of NLP tasks, including structured psychological profiling and fine-tuning large language models for depression detection. Comprehensive evaluations highlight the dataset’s effectiveness and practical value for depression risk identification and psychological analysis, thereby providing insights for mental health applications tailored to Chinese-speaking populations.

Depression remains a major global mental health concern, bringing a higher risk of suicide and growing social costs tied to mental disorders. Leveraging social media as a valuable source of emotional signals, we identify two limitations in current NLP-based depression detection frameworks: (1) prediction systems often lack clear, user-friendly explanations for predictions in Depression Detection, and (2) the computational and confidentiality demands of LLMs are misaligned with the need for dependable, privacy-focused small-scale deployments. To address these challenges, we introduce TinyMentalLLMs (TMLs), a compact framework that offers two key contributions: (a) the construction of a small yet representative dataset through psychology-based textometry, and (b) an efficient fine-tuning strategy centered on multiple aspects of depression. This design improves both accuracy and F1 scores in generative models with 0.5B and 1.5B parameters, consistently yielding over 20% performance gains across datasets. TMLs achieve results on par with, and deliver better text quality than, much larger state-of-the-art models.

Understanding the decision-making processes of large language models (LLMs) is essential for their trustworthy development and deployment, however, current interpretability methods often face challenges such as low resolution and high computational cost. To address these limitations, we propose the Multi-Layer Attention Consistency Score (MACS), a novel, lightweight, and easily deployable heuristic for estimating the importance of input tokens in decoder-based models. MACS measures contributions of input tokens based on the consistency of maximal attention. Empirical evaluations demonstrate that MACS achieves a favorable trade-off between interpretability quality and computational efficiency, showing faithfulness comparable to complex techniques with a 22% decrease in VRAM usage and 30% reduction in latency.