# Leveraging Large Language Models for Cost-Effective, Multilingual Depression Detection and Severity Assessment

arXiv: https://arxiv.org/abs/2504.04891 · HTML: https://mlsanity.com/paper/2504.04891/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 5 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [GPT-4V with Emotion: A Zero-shot Benchmark for Generalized Emotion Recognition](https://mlsanity.com/paper/2312.04293/) (2023)
- [GPT-4 Emulates Average-Human Emotional Cognition from a Third-Person Perspective](https://mlsanity.com/paper/2408.13718/) (2024)
- [Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Models](https://mlsanity.com/paper/2502.05879/) (2025)

## Built on by (most cited first, 1 of 1)

- [Innovating China's Intangible Cultural Heritage with DeepSeek + MidJourney: The Case of Yangliuqing theme Woodblock Prints](https://mlsanity.com/paper/2506.14104/) (2025)

## Abstract (arXiv)

Depression is a prevalent mental health disorder that is difficult to detect early due to subjective symptom assessments. Recent advancements in large language models have offered efficient and cost-effective approaches for this objective. In this study, we evaluated the performance of four LLMs in depression detection using clinical interview data. We selected the best performing model and further tested it in the severity evaluation scenario and knowledge enhanced scenario. The robustness was evaluated in complex diagnostic scenarios using a dataset comprising 51074 statements from six different mental disorders. We found that DeepSeek V3 is the most reliable and cost-effective model for depression detection, performing well in both zero-shot and few-shot scenarios, with zero-shot being the most efficient choice. The evaluation of severity showed low agreement with the human evaluator, particularly for mild depression. The model maintains stably high AUCs for detecting depression in complex diagnostic scenarios. These findings highlight DeepSeek V3s strong potential for text-based depression detection in real-world clinical applications. However, they also underscore the need for further refinement in severity assessment and the mitigation of potential biases to enhance clinical reliability.