Zero-shot Explainable Mental Health Analysis on Social Media by Incorporating Mental Scales

• URL: http://arxiv.org/abs/2402.10948v2
• Date: Fri, 15 Mar 2024 02:02:02 GMT
• Title: Zero-shot Explainable Mental Health Analysis on Social Media by Incorporating Mental Scales
• Authors: Wenyu Li, Yinuo Zhu, Xin Lin, Ming Li, Ziyue Jiang, Ziqian Zeng,
• Abstract summary: Mental Analysis by Incorporating Mental Scales (MAIMS) is inspired by the psychological assessment practice of using scales to evaluate mental states. First, the patient completes mental scales, and second, the psychologist interprets the collected information from the mental scales and makes informed decisions.
• Score: 23.94585145560042
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Traditional discriminative approaches in mental health analysis are known for their strong capacity but lack interpretability and demand large-scale annotated data. The generative approaches, such as those based on large language models (LLMs), have the potential to get rid of heavy annotations and provide explanations but their capabilities still fall short compared to discriminative approaches, and their explanations may be unreliable due to the fact that the generation of explanation is a black-box process. Inspired by the psychological assessment practice of using scales to evaluate mental states, our method which is called Mental Analysis by Incorporating Mental Scales (MAIMS), incorporates two procedures via LLMs. First, the patient completes mental scales, and second, the psychologist interprets the collected information from the mental scales and makes informed decisions. Experimental results show that MAIMS outperforms other zero-shot methods. MAIMS can generate more rigorous explanation based on the outputs of mental scales

Related papers

• MentalGLM Series: Explainable Large Language Models for Mental Health Analysis on Chinese Social Media [31.752563319585196]
  Black box models are inflexible when switching between tasks, and their results typically lack explanations. With the rise of large language models (LLMs), their flexibility has introduced new approaches to the field. In this paper, we introduce the first multi-task Chinese Social Media Interpretable Mental Health Instructions dataset, consisting of 9K samples. We also propose MentalGLM series models, the first open-source LLMs designed for explainable mental health analysis targeting Chinese social media.
  arXiv  Detail & Related papers  (2024-10-14T09:29:27Z)
• MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders [59.515827458631975]
  Mental health disorders are one of the most serious diseases in the world. Privacy concerns limit the accessibility of personalized treatment data. MentalArena is a self-play framework to train language models.
  arXiv  Detail & Related papers  (2024-10-09T13:06:40Z)
• Quantifying AI Psychology: A Psychometrics Benchmark for Large Language Models [57.518784855080334]
  Large Language Models (LLMs) have demonstrated exceptional task-solving capabilities, increasingly adopting roles akin to human-like assistants. This paper presents a framework for investigating psychology dimension in LLMs, including psychological identification, assessment dataset curation, and assessment with results validation. We introduce a comprehensive psychometrics benchmark for LLMs that covers six psychological dimensions: personality, values, emotion, theory of mind, motivation, and intelligence.
  arXiv  Detail & Related papers  (2024-06-25T16:09:08Z)
• WellDunn: On the Robustness and Explainability of Language Models and Large Language Models in Identifying Wellness Dimensions [46.60244609728416]
  Language Models (LMs) are being proposed for mental health applications where the heightened risk of adverse outcomes means predictive performance may not be a litmus test of a model's utility in clinical practice. We introduce an evaluation design that focuses on the robustness and explainability of LMs in identifying Wellness Dimensions (WDs) We reveal four surprising results about LMs/LLMs.
  arXiv  Detail & Related papers  (2024-06-17T19:50:40Z)
• LLM Questionnaire Completion for Automatic Psychiatric Assessment [49.1574468325115]
  We employ a Large Language Model (LLM) to convert unstructured psychological interviews into structured questionnaires spanning various psychiatric and personality domains. The obtained answers are coded as features, which are used to predict standardized psychiatric measures of depression (PHQ-8) and PTSD (PCL-C)
  arXiv  Detail & Related papers  (2024-06-09T09:03:11Z)
• PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents [68.50571379012621]
  Psychological measurement is essential for mental health, self-understanding, and personal development. PsychoGAT (Psychological Game AgenTs) achieves statistically significant excellence in psychometric metrics such as reliability, convergent validity, and discriminant validity.
  arXiv  Detail & Related papers  (2024-02-19T18:00:30Z)
• MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models [28.62967557368565]
  We build the first multi-task and multi-source interpretable mental health instruction dataset on social media, with 105K data samples. We use expert-written few-shot prompts and collected labels to prompt ChatGPT and obtain explanations from its responses. Based on the IMHI dataset and LLaMA2 foundation models, we train MentalLLaMA, the first open-source LLM series for interpretable mental health analysis.
  arXiv  Detail & Related papers  (2023-09-24T06:46:08Z)
• WellXplain: Wellness Concept Extraction and Classification in Reddit Posts for Mental Health Analysis [8.430481660019451]
  In traditional therapy sessions, professionals manually pinpoint the origins and outcomes of underlying mental challenges. We introduce an approach to this intricate mental health analysis by framing the identification of wellness dimensions in Reddit content as a wellness concept extraction and categorization challenge. We've curated a unique dataset named WELLXPLAIN, comprising 3,092 entries and totaling 72,813 words.
  arXiv  Detail & Related papers  (2023-08-25T23:50:05Z)
• Towards Interpretable Mental Health Analysis with Large Language Models [27.776003210275608]
  We evaluate the mental health analysis and emotional reasoning ability of large language models (LLMs) on 11 datasets across 5 tasks. Based on prompts, we explore LLMs for interpretable mental health analysis by instructing them to generate explanations for each of their decisions. We convey strict human evaluations to assess the quality of the generated explanations, leading to a novel dataset with 163 human-assessed explanations.
  arXiv  Detail & Related papers  (2023-04-06T19:53:59Z)
• Memory-Augmented Theory of Mind Network [59.9781556714202]
  Social reasoning requires the capacity of theory of mind (ToM) to contextualise and attribute mental states to others. Recent machine learning approaches to ToM have demonstrated that we can train the observer to read the past and present behaviours of other agents. We tackle the challenges by equipping the observer with novel neural memory mechanisms to encode, and hierarchical attention to selectively retrieve information about others. This results in ToMMY, a theory of mind model that learns to reason while making little assumptions about the underlying mental processes.
  arXiv  Detail & Related papers  (2023-01-17T14:48:58Z)
• Comparing interpretation methods in mental state decoding analyses with deep learning models [8.00426138461057]
  We compare the explanations of prominent interpretation methods for the mental state decoding decisions of DL models trained on three fMRI datasets. We find that interpretation methods that focus on how sensitively a model's decoding decision changes with the values of the input produce explanations that better match with the results of a standard general linear model analysis of the fMRI data.
  arXiv  Detail & Related papers  (2022-05-31T07:43:02Z)

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.