Szczegóły publikacji
Opis bibliograficzny
MentalChat16K: a benchmark dataset for conversational mental health assistance / Jia Xu, Tianyi Wei, Bojian Hou, Patryk ORZECHOWSKI, Shu Yang, Ruochen Jin, Rachael Paulbeck, Joost Wagenaar, George Demiris, Li Shen // W: KDD '25 [Dokument elektroniczny] : proceedings of the 31st ACM SIGKDD conference on Knowledge Discovery and Data Mining : Toronto, ON, Canada, August 3-7, 2025 , V.2 . — Wersja do Windows. — Dane tekstowe. — New York : Association for Computing Machinery, cop. 2025. — e-ISBN: 979-8-4007-1454-2. — S. 5367-5378. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 5375-5376, Abstr. — Publikacja dostępna online od: 2025-08-03. — P. Orzechowski - dod. afiliacja: University of Pennsylvania, Philadelphia, Pennsylvania, USA
Autorzy (10)
- Xu Jia
- Wei Tianyi
- Hou Bojian
- AGHOrzechowski Patryk
- Yang Shu
- Jin Ruochen
- Paulbeck Rachael
- Wagenaar Joost
- Demiris George
- Shen Li
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 165840 |
|---|---|
| Data dodania do BaDAP | 2026-03-03 |
| Tekst źródłowy | URL |
| DOI | 10.1145/3711896.3737393 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Association for Computing Machinery (ACM) |
| Konferencja | ACM International Conference on Knowledge Discovery and Data Mining 2025 |
Abstract
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/PennShenLab/MentalChat16K.