MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Xu, Jia Wei, Tianyi Hou, Bojian Orzechowski, Patryk Yang, Shu Jin, Ruochen Paulbeck, Rachael Wagenaar, Joost Demiris, George Shen, Li |
| author_facet | Xu, Jia Wei, Tianyi Hou, Bojian Orzechowski, Patryk Yang, Shu Jin, Ruochen Paulbeck, Rachael Wagenaar, Joost Demiris, George Shen, Li |
| contents | We introduce MentalChat16K, an English benchmark dataset combining a synthetic mental health counseling dataset and a dataset of anonymized transcripts from interventions between Behavioral Health Coaches and Caregivers of patients in palliative or hospice care. Covering a diverse range of conditions like depression, anxiety, and grief, this curated dataset is designed to facilitate the development and evaluation of large language models for conversational mental health assistance. By providing a high-quality resource tailored to this critical domain, MentalChat16K aims to advance research on empathetic, personalized AI solutions to improve access to mental health support services. The dataset prioritizes patient privacy, ethical considerations, and responsible data usage. MentalChat16K presents a valuable opportunity for the research community to innovate AI technologies that can positively impact mental well-being. The dataset is available at https://huggingface.co/datasets/ShenLab/MentalChat16K and the code and documentation are hosted on GitHub at https://github.com/ChiaPatricia/MentalChat16K. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13509 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance Xu, Jia Wei, Tianyi Hou, Bojian Orzechowski, Patryk Yang, Shu Jin, Ruochen Paulbeck, Rachael Wagenaar, Joost Demiris, George Shen, Li Machine Learning Artificial Intelligence Computation and Language Computers and Society Human-Computer Interaction We introduce MentalChat16K, an English benchmark dataset combining a synthetic mental health counseling dataset and a dataset of anonymized transcripts from interventions between Behavioral Health Coaches and Caregivers of patients in palliative or hospice care. Covering a diverse range of conditions like depression, anxiety, and grief, this curated dataset is designed to facilitate the development and evaluation of large language models for conversational mental health assistance. By providing a high-quality resource tailored to this critical domain, MentalChat16K aims to advance research on empathetic, personalized AI solutions to improve access to mental health support services. The dataset prioritizes patient privacy, ethical considerations, and responsible data usage. MentalChat16K presents a valuable opportunity for the research community to innovate AI technologies that can positively impact mental well-being. The dataset is available at https://huggingface.co/datasets/ShenLab/MentalChat16K and the code and documentation are hosted on GitHub at https://github.com/ChiaPatricia/MentalChat16K. |
| title | MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance |
| topic | Machine Learning Artificial Intelligence Computation and Language Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2503.13509 |