Large language models for mental health

• URL: http://arxiv.org/abs/2411.11880v1
• Date: Mon, 04 Nov 2024 14:02:00 GMT
• Title: Large language models for mental health
• Authors: Andreas Triantafyllopoulos, Yannik Terhorst, Iosif Tsangko, Florian B. Pokorny, Katrin D. Bartl-Pokorny, Lennart Seizer, Ayal Klein, Jenny Chim, Dana Atzil-Slonim, Maria Liakata, Markus Bühner, Johanna Löchner, Björn Schuller,
• Abstract summary: Digital technologies have long been explored as a complement to standard procedure in mental health research and practice. The recent emergence of large language models (LLMs) 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.
• Score: 10.592145325363266
• License:
• 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.

Related papers

• A Survey on Large Language Models with Multilingualism: Recent Advances and New Frontiers [48.314619377988436]
  The rapid development of Large Language Models (LLMs) demonstrates remarkable multilingual capabilities in natural language processing. Despite the breakthroughs of LLMs, the investigation into the multilingual scenario remains insufficient. This survey aims to help the research community address multilingual problems and provide a comprehensive understanding of the core concepts, key techniques, and latest developments in multilingual natural language processing based on LLMs.
  arXiv  Detail & Related papers  (2024-05-17T17:47:39Z)
• Large Language Model for Mental Health: A Systematic Review [2.9429776664692526]
  Large language models (LLMs) have attracted significant attention for potential applications in digital health. This systematic review focuses on their strengths and limitations in early screening, digital interventions, and clinical applications.
  arXiv  Detail & Related papers  (2024-02-19T17:58:41Z)
• Rethinking Interpretability in the Era of Large Language Models [76.1947554386879]
  Large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks. The capability to explain in natural language allows LLMs to expand the scale and complexity of patterns that can be given to a human. These new capabilities raise new challenges, such as hallucinated explanations and immense computational costs.
  arXiv  Detail & Related papers  (2024-01-30T17:38:54Z)
• Benefits and Harms of Large Language Models in Digital Mental Health [40.02859683420844]
  Large language models (LLMs) show promise in leading digital mental health to uncharted territory. This article presents contemporary perspectives on the opportunities and risks posed by LLMs in the design, development, and implementation of digital mental health tools.
  arXiv  Detail & Related papers  (2023-11-07T14:11:10Z)
• Let Models Speak Ciphers: Multiagent Debate through Embeddings [84.20336971784495]
  We introduce CIPHER (Communicative Inter-Model Protocol Through Embedding Representation) to address this issue. By deviating from natural language, CIPHER offers an advantage of encoding a broader spectrum of information without any modification to the model weights. This showcases the superiority and robustness of embeddings as an alternative "language" for communication among LLMs.
  arXiv  Detail & Related papers  (2023-10-10T03:06:38Z)
• A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics [32.10937977924507]
  The utilization of large language models (LLMs) in the Healthcare domain has generated both excitement and concern. This survey outlines the capabilities of the currently developed LLMs for Healthcare and explicates their development process.
  arXiv  Detail & Related papers  (2023-10-09T13:15:23Z)
• Redefining Digital Health Interfaces with Large Language Models [69.02059202720073]
  Large Language Models (LLMs) have emerged as general-purpose models with the ability to process complex information. We show how LLMs can provide a novel interface between clinicians and digital technologies. We develop a new prognostic tool using automated machine learning.
  arXiv  Detail & Related papers  (2023-10-05T14:18:40Z)
• A Survey of Large Language Models [81.06947636926638]
  Language modeling has been widely studied for language understanding and generation in the past two decades. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
  arXiv  Detail & Related papers  (2023-03-31T17:28:46Z)
• Shortcut Learning of Large Language Models in Natural Language Understanding [119.45683008451698]
  Large language models (LLMs) have achieved state-of-the-art performance on a series of natural language understanding tasks. They might rely on dataset bias and artifacts as shortcuts for prediction. This has significantly affected their generalizability and adversarial robustness.
  arXiv  Detail & Related papers  (2022-08-25T03:51:39Z)
• Artificial Intelligence for Global Health: Learning From a Decade of Digital Transformation in Health Care [0.0]
  Low-and-middle income countries (LMICs) have already been undergoing a digital transformation of their own in health care over the last decade. With the introduction of new technologies, it is common to start afresh with a top-down approach, and implement these technologies in isolation, leading to lack of use and a waste of resources. This paper outlines the necessary considerations both from the perspective of current gaps in research, as well as from the lived experiences of health care professionals in resource-limited settings.
  arXiv  Detail & Related papers  (2020-05-20T23:50:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.