  In an era where the silent struggle of underdiagnosed depression pervades
globally, our research delves into the crucial link between mental health and
social media. This work focuses on early detection of depression, particularly
in extroverted social media users, using LLMs such as GPT 3.5, GPT 4 and our
proposed GPT 3.5 fine-tuned model DepGPT, as well as advanced Deep learning
models(LSTM, Bi-LSTM, GRU, BiGRU) and Transformer models(BERT, BanglaBERT,
SahajBERT, BanglaBERT-Base). The study categorized Reddit and X datasets into
"Depressive" and "Non-Depressive" segments, translated into Bengali by native
speakers with expertise in mental health, resulting in the creation of the
Bengali Social Media Depressive Dataset (BSMDD). Our work provides full
architecture details for each model and a methodical way to assess their
performance in Bengali depressive text categorization using zero-shot and
few-shot learning techniques. Our work demonstrates the superiority of
SahajBERT and Bi-LSTM with FastText embeddings in their respective domains also
tackles explainability issues with transformer models and emphasizes the
effectiveness of LLMs, especially DepGPT, demonstrating flexibility and
competence in a range of learning contexts. According to the experiment
results, the proposed model, DepGPT, outperformed not only Alpaca Lora 7B in
zero-shot and few-shot scenarios but also every other model, achieving a
near-perfect accuracy of 0.9796 and an F1-score of 0.9804, high recall, and
exceptional precision. Although competitive, GPT-3.5 Turbo and Alpaca Lora 7B
show relatively poorer effectiveness in zero-shot and few-shot situations. The
work emphasizes the effectiveness and flexibility of LLMs in a variety of
linguistic circumstances, providing insightful information about the complex
field of depression detection models.
