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Towards Interpretable Mental Health Analysis with Large Language Models
Open accessAbstract
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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“Prompting to concentrate on the emotional clues in text was also shown to improve ChatGPT performance on a variety of mental health-related tasks [71].”
From The Opportunities and Risks of Large Language Models in Mental Health (2024) · cites this paper as [71]
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“ChatGPT has been shown to be able to explain why an individual was classified as experiencing stress or depressive symptoms [71]”
From The Opportunities and Risks of Large Language Models in Mental Health (2024) · cites this paper as [71]
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Self-citation
Metadata
- 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)
- Venue
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Published
- 2023
- Pages
- 6056-6077
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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