The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models
Open accessAbstract
As an integral part of people's daily lives, social media is becoming a rich source for automatic mental health analysis. As traditional discriminative methods bear poor generalization ability and low interpretability, the recent large language models (LLMs) have been explored for interpretable mental health analysis on social media, which aims to provide detailed explanations along with predictions in zero-shot or few-shot settings. The results show that LLMs still achieve unsatisfactory classification performance in a zero-shot/few-shot manner, which further significantly affects the quality of the generated explanations. Domain-specific finetuning is an effective solution, but faces two critical challenges: 1) lack of high-quality training data. 2) no open-source foundation LLMs. To alleviate these problems, we formally model interpretable mental health analysis as a text generation task, and build the first multi-task and multi-source interpretable mental health instruction (IMHI) dataset with 105K data samples to support LLM instruction tuning and evaluation. The raw social media data are collected from 10 existing sources covering 8 mental health analysis tasks. We prompt ChatGPT with expert-designed few-shot prompts to obtain explanations. To ensure the reliability of the explanations, we perform strict automatic and human evaluations on the correctness, consistency, and quality of generated data. Based on the IMHI dataset and LLaMA2 foundation models, we train MentaLLaMA, the first open-source instruction-following LLM series for interpretable mental health analysis on social media. We evaluate MentaLLaMA and other advanced methods on the IMHI benchmark, the first holistic evaluation benchmark for interpretable mental health analysis. The results show that MentaLLaMA approaches state-of-the-art discriminative methods in correctness and generates human-level explanations. MentaLLaMA models also show strong generalizability to unseen tasks. The project is available at https://github.com/SteveKGYang/MentaLLaMA.
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Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2019) · arXiv · doi:10.48550/arxiv.1910.13461 · Cited by 8,816
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208
Self-Instruct: Aligning Language Models with Self-Generated Instructions
Wang, Y., Kordi, Y., Mishra, S., Liu, A., Smith, N. A., Khashabi, D., & Hajishirzi, H. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.754 · Cited by 1,243
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Dao, T., Fu, D. Y., Ermon, S., Rudra, A., & Ré, C. (2022) · arXiv · doi:10.48550/arxiv.2205.14135 · Cited by 1,214
Evans-Lacko, S., Aguilar-Gaxiola, S., Al-Hamzawi, A., Alonso, J., Benjet, C., Bruffaerts, R., Chiu, W. T., Florescu, S., de Girolamo, G., Gureje, O., et al. (2018) · Psychological Medicine · doi:10.1017/s0033291717003336 · Cited by 781 · cited 1×
“Mental health-related issues are posing increasing threats to public health worldwide [9], but remain underestimated due to the lack of social awareness and stigma [30].”
BackgroundLearning to summarize from human feedback
Stiennon, N., Ouyang, L., Wu, J., Ziegler, D. M., Lowe, R., Voss, C., Radford, A., Amodei, D., & Christiano, P. (2020) · arXiv · doi:10.48550/arxiv.2009.01325 · Cited by 550
Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data
Xu, X., Yao, B., Dong, Y., Gabriel, S., Yu, H., Hendler, J., Ghassemi, M., Dey, A. K., & Wang, D. (2024) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3643540 · Cited by 278
Ethical research protocols for social media health research
Benton, A., Coppersmith, G., & Dredze, M. (2017) · Proceedings of the First ACL Workshop on Ethics in Natural Language Processing · doi:10.18653/v1/w17-1612 · Cited by 219
Towards Interpretable Mental Health Analysis with Large Language Models
Yang, K., Ji, S., Zhang, T., Xie, Q., Kuang, Z., & Ananiadou, S. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.370 · Cited by 155
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Cited by
Large language models in healthcare and medical domain: A review
Nazi, Z. A., & Peng, W. (2024) · Informatics · doi:10.3390/informatics11030057 · Cited by 415
Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data
Xu, X., Yao, B., Dong, Y., Gabriel, S., Yu, H., Hendler, J., Ghassemi, M., Dey, A. K., & Wang, D. (2024) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3643540 · Cited by 278
A Review on Edge Large Language Models: Design, Execution, and Applications
Zheng, Y., Chen, Y., Qian, B., Shi, X., Shu, Y., & Chen, J. (2025) · ACM Computing Surveys · doi:10.1145/3719664 · Cited by 169
The Applications of Large Language Models in Mental Health: Scoping Review
Jin, Y., Liu, J., Li, P., Wang, B., Yan, Y., Zhang, H., Ni, C., Wang, J., Li, Y., Bu, Y., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/69284 · Cited by 94
Large Language Models in Mental Health Care: A Scoping Review
Hua, Y., Liu, F., Yang, K., Li, Z., Na, H., Sheu, Y.-H., Zhou, P., Moran, L. V., Ananiadou, S., Clifton, D. A., et al. (2025) · Current Treatment Options in Psychiatry · doi:10.1007/s40501-025-00363-y · Cited by 92
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Metadata
- Authors
- Yang, K., Zhang, T., Kuang, Z., Xie, Q., Huang, J., & Ananiadou, S.
- Venue
- Proceedings of the ACM Web Conference 2024
- Publisher
- ACM
- Published
- 2024-05-13
- Pages
- 4489-4500
- OpenAlex
- W4396723505
- Cited by
- 167
- References
- 10
- FWCI
- 115.52
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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