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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.11880 |
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| _version_ | 1866910704033857536 |
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| author | Triantafyllopoulos, Andreas Terhorst, Yannik Tsangko, Iosif Pokorny, Florian B. Bartl-Pokorny, Katrin D. Seizer, Lennart Klein, Ayal Chim, Jenny Atzil-Slonim, Dana Liakata, Maria Bühner, Markus Löchner, Johanna Schuller, Björn |
| author_facet | Triantafyllopoulos, Andreas Terhorst, Yannik Tsangko, Iosif Pokorny, Florian B. Bartl-Pokorny, Katrin D. Seizer, Lennart Klein, Ayal Chim, Jenny Atzil-Slonim, Dana Liakata, Maria Bühner, Markus Löchner, Johanna Schuller, Björn |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_11880 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large language models for mental health Triantafyllopoulos, Andreas Terhorst, Yannik Tsangko, Iosif Pokorny, Florian B. Bartl-Pokorny, Katrin D. Seizer, Lennart Klein, Ayal Chim, Jenny Atzil-Slonim, Dana Liakata, Maria Bühner, Markus Löchner, Johanna Schuller, Björn Computers and Society 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. |
| title | Large language models for mental health |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2411.11880 |