Depression is the leading cause of global disability and early detection is crucial for effective intervention. Recent advances in large language models (LLMs) offer potential for analyzing text to identify depression symptoms. This work investigates the zero-shot capability of LLMs to recognize nine DSM5 depression symptoms from short-text inputs. We evaluated eight open LLMs with model sizes ranging from 1.5B to 14B parameters using a clinically annotated dataset and assessed both overall agreement and symptom-level performance. Results indicate that while smaller models exhibit limited clinical accuracy, the Qwen 2.5-7B model achieves substantial performance with a Cohen’s Kappa of 0.603 and a Macro F1 score of 0.648. Notably, a performance plateau between the 7B and 14B Qwen variants suggests that model scaling alone does not guarantee improved symptom-level classification, establishing Qwen 2.5-7B as a resource-efficient model. Further analysis of the best-performing model revealed strengths in identifying salient symptoms like suicidal thoughts, but limitations in recognizing core symptoms such as depressed mood and anhedonia. Misclassification analysis reveals that the model frequently misclassifies posts expressing ’depressed mood’ as ’no symptom’ or vice versa, often overlooking indicators of irritability or social withdrawal. These findings suggest that resource-efficient LLMs can support preliminary symptom screening in zero shot settings, but there is risk of overlooking clinically important symptoms without fine-tuning.

Social anxiety represents a prevalent challenge in modern society, affecting individuals across personal and professional spheres. Left unaddressed, this condition can yield substantial negative consequences, impacting social interactions and performance. Further understanding its diverse physical and emotional symptoms becomes pivotal for comprehensive diagnosis and tailored therapeutic interventions. This study analyze prev lance and frequency of social anxiety symptoms taken from Mayo Clinic, exploring diverse human experiences from utilizing a large Reddit dataset dedicated to this issue. Leveraging these platforms, the research aims to extract insights and examine a spectrum of physical and emotional symptoms linked to social anxiety disorder. Upholding ethical considerations, the study maintains strict user anonymity within the dataset. By employing a novel approach, the research utilizes BART-based multi-label zero-shot classification to identify and measure symptom prevalence and significance in the form of probability score for each symptom under consideration. Results uncover distinctive patterns: “Trembling” emerges as a prevalent physical symptom, while emotional symptoms like “Fear of being judged negatively” exhibit high frequencies. These findings offer insights into the multifaceted nature of social anxiety, aiding clinical practices and interventions tailored to its diverse expressions.