  Mental health challenges pose considerable global burdens on individuals and
communities. Recent data indicates that more than 20% of adults may encounter
at least one mental disorder in their lifetime. On the one hand, the
advancements in large language models have facilitated diverse applications,
yet a significant research gap persists in understanding and enhancing the
potential of large language models within the domain of mental health. On the
other hand, across various applications, an outstanding question involves the
capacity of large language models to comprehend expressions of human mental
health conditions in natural language. This study presents an initial
evaluation of large language models in addressing this gap. Due to this, we
compare the performance of Llama-2 and ChatGPT with classical Machine as well
as Deep learning models. Our results on the DAIC-WOZ dataset show that
transformer-based models, like BERT or XLNet, outperform the large language
models.
