The prevalence of mental health issues is increasing, affecting millions of people worldwide. Currently, most individuals requiring mental health services do not receive any form of treatment, with even greater limitations in access for ethnic minorities, low-income populations, and rural residents. In many cases, mental health issues can be effectively treated or, at times, prevented through early detection and intervention. However, many individuals do not receive the support they require until their mental health concerns have escalated.

NLP has made remarkable progress in recent years, driven by breakthroughs in large language models (LLMs) and the availability of large-scale datasets such as data from social media posts, online forums, and patient records. These advances have made NLP models highly capable of extracting valuable insights from text data related to mental health. This development raises two natural questions: (1) How well, if at all, can NLP enable early detection, diagnosis, and intervention, not only for patients or support seekers but also for therapists or support providers? (2) Can NLP-driven solutions help bridge the gap between the escalating demand for mental health resources and the limited availability of mental health professionals, providing scalable and immediate support through chatbots, virtual therapists, and data- driven interventions? Both questions touch upon the technical feasibility as well as the ethical concerns about the use of a developing technology in a sensitive application.

In this Dagstuhl Seminar, we will underscore key areas in which NLP has the potential to profoundly enhance mental health treatments, including but not limited to (1) understanding how mental states change and how therapeutic change occurs; (2) how NLP can help therapist training and feedback; (3) technological gaps, privacy and multilingual issues; (4) evaluation, validation and ethical concerns.

Our seminar has two concrete objectives: (1) Joint research publications or collaboration opportunities, such as position papers that outline current challenges and opportunities around how to build responsible and robust NLP models to improve mental health. Some deliverables include compiling a report around outcomes associated with each topic; (2) Formation of a cross-field community for NLP for mental health, both in NLP venues as well as the psychotherapy communities, to bridge the two communities for interdisciplinary work.

Creative Commons BY 4.0

Dana Atzil-Slonim, Iryna Gurevych, Dirk Hovy, Vivek Srikumar, and Diyi Yang