An Assessment on Comprehending Mental Health through Large Language Models
This work addresses a research gap in applying large language models to mental health, but it is incremental as it primarily compares existing models without introducing new methods.
This study tackled the problem of evaluating large language models' ability to comprehend mental health conditions from natural language, finding that transformer-based models like BERT or XLNet outperformed large language models such as Llama-2 and ChatGPT on the DAIC-WOZ dataset.
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.