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.

翻译：心理健康挑战给全球个人和社区带来了巨大负担。最新数据显示，超过20%的成年人一生中可能至少经历一种心理障碍。一方面，大语言模型的进步推动了多种应用，但在理解和提升大语言模型在心理健康领域的潜力方面仍存在显著的研究空白。另一方面，在各种应用中，一个突出的问题是大语言模型能否理解自然语言中人类心理健康状态的表达。本研究初步评估了大语言模型在填补这一空白方面的能力。为此，我们比较了Llama-2和ChatGPT与经典机器学习及深度学习模型的性能。在DAIC-WOZ数据集上的结果表明，基于Transformer的模型（如BERT或XLNet）优于大语言模型。