用微调大模型在社交平台高效识别抑郁内容
Advancing Depression Detection on Social Media Platforms Through Fine-Tuned Large Language Models
- 微调GPT-3.5 Turbo和LLaMA2-7B处理社交媒体文本
- 抑郁内容识别准确率达96.0%
- 适合心理健康监测与早期干预研究者
本研究探讨了利用大型语言模型（LLMs）从用户社交媒体数据中提升抑郁检测效果。通过使用微调后的GPT 3.5 Turbo 1106和LLaMA2-7B模型，结合前期研究的大规模数据集，能够在社交媒体帖子中以近96.0%的高准确率识别出抑郁相关内容。与文献中现有先进系统相比，所提出的微调LLM方法表现出更优性能，验证了基于大模型的微调系统在抑郁检测中的鲁棒性与潜力。研究详细描述了参数设置与微调流程，并讨论了其在多个社交平台实现抑郁症早期诊断的重要意义。
原文摘要 · Abstract (English)
This study investigates the use of Large Language Models (LLMs) for improved depression detection from users social media data. Through the use of fine-tuned GPT 3.5 Turbo 1106 and LLaMA2-7B models and a sizable dataset from earlier studies, we were able to identify depressed content in social media posts with a high accuracy of nearly 96.0 percent. The comparative analysis of the obtained results with the relevant studies in the literature shows that the proposed fine-tuned LLMs achieved enhanced performance compared to existing state of the-art systems. This demonstrates the robustness of LLM-based fine-tuned systems to be used as potential depression detection systems. The study describes the approach in depth, including the parameters used and the fine-tuning procedure, and it addresses the important implications of our results for the early diagnosis of depression on several social media platforms.
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