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Advances in large language models (LLMs) have empowered a variety ofapplications. However, there is still a significant gap in research when itcomes to understanding and enhancing the capabilities of LLMs in the field ofmental health. In this work, we present the first comprehensive evaluation ofmultiple LLMs, including Alpaca, Alpaca-LoRA, FLAN-T5, GPT-3.5, and GPT-4, onvarious mental health prediction tasks via online text data. We conduct a broadrange of experiments, covering zero-shot prompting, few-shot prompting, andinstruction fine-tuning. The results indicate a promising yet limitedperformance of LLMs with zero-shot and few-shot prompt designs for the mentalhealth tasks. More importantly, our experiments show that instructionfinetuning can significantly boost the performance of LLMs for all taskssimultaneously. Our best-finetuned models, Mental-Alpaca and Mental-FLAN-T5,outperform the best prompt design of GPT-3.5 (25 and 15 times bigger) by 10.9%on balanced accuracy and the best of GPT-4 (250 and 150 times bigger) by 4.8%.They further perform on par with the state-of-the-art task-specific languagemodel. We also conduct an exploratory case study on LLMs' capability on themental health reasoning tasks, illustrating the promising capability of certainmodels such as GPT-4. We summarize our findings into a set of action guidelinesfor potential methods to enhance LLMs' capability for mental health tasks.Meanwhile, we also emphasize the important limitations before achievingdeployability in real-world mental health settings, such as known racial andgender bias. We highlight the important ethical risks accompanying this line ofresearch.

Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data