Cited by 155Mental Health via WritingExplainable Artificial Intelligence (XAI)Emotion and Mood Recognition

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Towards Interpretable Mental Health Analysis with Large Language Models

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Yang, Kailai, Ji, Shaoxiong, Zhang, Tianlin, Xie, Qianqian, Kuang, Ziyan, Ananiadou, Sophia·2023·Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

DOI 10.18653/v1/2023.emnlp-main.370

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Abstract

The latest large language models (LLMs) such as ChatGPT, exhibit strong capabilities in automated mental health analysis.However, existing relevant studies bear several limitations, including inadequate evaluations, lack of prompting strategies, and ignorance of exploring LLMs for explainability.To bridge these gaps, we comprehensively evaluate the mental health analysis and emotional reasoning ability of LLMs on 11 datasets across 5 tasks.We explore the effects of different prompting strategies with unsupervised and distantly supervised emotional information.Based on these 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 humanassessed explanations 1 .We benchmark existing automatic evaluation metrics on this dataset to guide future related works.According to the results, ChatGPT shows strong in-context learning ability but still has a significant gap with advanced task-specific methods.Careful prompt engineering with emotional cues and expertwritten few-shot examples can also effectively improve performance on mental health analysis.In addition, ChatGPT generates explanations that approach human performance, showing its great potential in explainable mental health analysis.

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Authors: Yang, K., Ji, S., Zhang, T., Xie, Q., Kuang, Z., & Ananiadou, S.
Affiliations: University of Manchester (GB)
University of Helsinki (FI)
Yale University (US)
ORCID: Yang, Kailai
Ji, Shaoxiong
Zhang, Tianlin
Xie, Qianqian
Kuang, Ziyan
Ananiadou, Sophia
Venue: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Publisher: Association for Computational Linguistics
Published: 2023
Pages: 6056-6077
DOI: 10.18653/v1/2023.emnlp-main.370
OpenAlex: W4389523980
Cited by: 155
References: 71
Citations by year: 2026: 32 · 2025: 58 · 2024: 56 · 2023: 9
FWCI: 77.54
Type: conference paper
Language: en
Open access: hybrid
doi.org
Licence: cc-by
Locations: Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
arXiv (Cornell University) (submittedVersion, cc-by)
Työväentutkimus Vuosikirja (submittedVersion)
arXiv (Cornell University) (cc-by)
Funders: Government of the United Kingdom
UK Research and Innovation
European Commission (HORIZON2020, 771113, 101070350)
China Scholarship Council
HORIZON EUROPE Framework Programme
Biotechnology and Biological Sciences Research Council (BB/P025684/1)
Topics: Social Sciences › Psychology › Social Psychology › Mental Health via Writing
Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
Keywords: large language models, explainable AI, prompt engineering, emotional reasoning, explanation generation, ChatGPT, human evaluation of explanations, emotional cues, in-context learning

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Towards Interpretable Mental Health Analysis with Large Language Models

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