Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data
Author(s)
Xu, Xuhai
Yao, Bingsheng
Dong, Yuanzhe
Gabriel, Saadia
Yu, Hong
Hendler, James
Ghassemi, Marzyeh
Dey, Anind K.
Wang, Dakuo
Date Issued
March 6, 2024
Journal
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Publisher
Association for Computing Machinery
Citation
Xuhai Xu, Bingsheng Yao, Yuanzhe Dong, Saadia Gabriel, Hong Yu, James Hendler, Marzyeh Ghassemi, Anind K. Dey, and Dakuo Wang. 2024. Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 8, 1, Article 31 (March 2024), 32 pages.
Version
Final published version
Abstract
Advances in large language models (LLMs) have empowered a variety of applications. However, there is still a significant gap in research when it comes to understanding and enhancing the capabilities of LLMs in the field of mental health. In this work, we present a comprehensive evaluation of multiple LLMs on various mental health prediction tasks via online text data, including Alpaca, Alpaca-LoRA, FLAN-T5, GPT-3.5, and GPT-4. We conduct a broad range of experiments, covering zero-shot prompting, few-shot prompting, and instruction fine-tuning. The results indicate a promising yet limited performance of LLMs with zero-shot and few-shot prompt designs for mental health tasks. More importantly, our experiments show that instruction finetuning can significantly boost the performance of LLMs for all tasks simultaneously. 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 language model. We also conduct an exploratory case study on LLMs' capability on mental health reasoning tasks, illustrating the promising capability of certain models such as GPT-4. We summarize our findings into a set of action guidelines for potential methods to enhance LLMs' capability for mental health tasks. Meanwhile, we also emphasize the important limitations before achieving deployability in real-world mental health settings, such as known racial and gender bias. We highlight the important ethical risks accompanying this line of research.
Subjects
Computer Networks and Communications
Hardware and Architecture
Human-Computer Interaction
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3643540
