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Large language models for mental health

Triantafyllopoulos A, Terhorst Y, Tsangko I, Pokorny FB, Bartl-Pokorny KD, Seizer L, Seizer L, Klein A, Chim J, Atzil-Slonim D, Liakata M, Buehner M, Löchner J, Schuller B (2025)

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Publication Status: Submitted

Publication Type: Unpublished / Preprint

Future Publication Type: Journal article

Publication year: 2025

Publisher: arXiv

DOI: 10.48550/arXiv.2411.11880

Abstract

Digital technologies have long been explored as a complement to standard procedure in mental health research and practice, ranging from the management of electronic health records to app-based interventions. The recent emergence of large language models (LLMs), both proprietary and open-source ones, represents a major new opportunity on that front. Yet there is still a divide between the community developing LLMs and the one which may benefit from them, thus hindering the beneficial translation of the technology into clinical use. This divide largely stems from the lack of a common language and understanding regarding the technology's inner workings, capabilities, and risks. Our narrative review attempts to bridge this gap by providing intuitive explanations behind the basic concepts related to contemporary LLMs.

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How to cite

APA:

Triantafyllopoulos, A., Terhorst, Y., Tsangko, I., Pokorny, F.B., Bartl-Pokorny, K.D., Seizer, L.,... Schuller, B. (2025). Large language models for mental health. (Unpublished, Submitted).

MLA:

Triantafyllopoulos, Andreas, et al. Large language models for mental health. Unpublished, Submitted. 2025.

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