Towards Interpretable Mental Health Analysis with Large Language Models (2023)

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 human-assessed explanations. 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 expert-written 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.

## introduction warning: this paper contains examples and descriptions that are depressive in nature. mental health conditions such as depression and suicidal ideation seriously challenge global health care @xcite . nlp researchers have devoted much effort to automatic mental health analysis, with current mainstream methods leveraging the pre-trained language models (plms) @xcite . most recently large language models (llms) @xcite , especially chatgpt foot_0 and gpt-4 (openai, 2023), have exhibited strong general language processing ability @xcite @xcite . in mental health analysis, lamichhane (2023) evaluated chatgpt on stress, depression, and suicide detection and glimpsed its strong language understanding ability to mental healthrelated texts. amin et al. (2023) compared the zeroshot performance of chatgpt on suicide and depression detection with previous fine-tuning-based methods. though previous works depict a promising future for a new llm-based paradigm in mental health analysis, several issues remain unresolved. firstly, mental health condition detection is a safecritical task requiring careful evaluation and high transparency for any predictions @xcite , while these works simply tested on a few binary mental health condition detection tasks and lack the explainability on detection results. moreover, other important mental health analysis tasks, such as the cause/factor detection of mental health conditions @xcite , were ignored. secondly, previous works mostly use simple prompts to detect mental health conditions directly. these vanilla methods ignore useful information, especially emotional cues, which are widely utilized for mental health analysis in previous works @xcite . we believe it requires a comprehensive exploration and evaluation of the ability and explainability of llms on mental health analysis, including mental health detection, emotional reasoning, and cause detection of mental health conditions. therefore, we raise the following three research questions (rq): to respond to these research questions, we first conduct a preliminary study of how llms perform on mental health analysis and emotional reasoning. we evaluate four llms with varying model sizes including chatgpt, instructgpt-3 @xcite , llama-13b, and llama-7b @xcite , on 11 datasets across 5 tasks including binary/multi-class mental health condition detection, cause/factor detection of mental health conditions, emotion recognition in conversations, and causal emotion entailment. we then delve into the effectiveness of different prompting strategies on mental health analysis, including zero-shot prompting, chain-of-thought (cot) prompting @xcite , emotion-enhanced prompting, and few-shot emotion-enhanced prompting. finally, we explore how llms perform for interpretable mental health analysis, where we instruct two representative llms: chatgpt and instructgpt-3, to generate natural language explanations for each of its results on mental health analysis. to assess the quality of llms-generated explanations, we perform human evaluations by following a strict annotation protocol designed by domain experts, and thus create the novel dataset with 163 humanassessed explanations of posts from llms, aimed at facilitating the investigating of explainable mental health analysis methods and automatic evaluation metrics. we benchmark numerous existing automatic evaluation metrics on the corpus to guide future research on automatically evaluating explainable mental health analysis. we conclude our findings as follows: ## methodology this section introduces the details of evaluated llms and different prompting strategies for improving llms' efficiency and explainability in mental health analysis. due to the page limits, all evaluations, experiments, and analyses on emotional reasoning are presented in appendix b. we also perform human evaluations on the quality of llm-generated explanations and benchmark existing automatic evaluation metrics on the human evaluation results, where an example is shown in figure 1 . ## experimental settings mental health analysis firstly, we introduce the benchmark datasets, baseline models, and automatic evaluation metrics for the classification results of mental health analysis. ## results and analysis we conduct all llama experiments on a single nvidia tesla a100 gpu with 80gb of memory. instructgpt-3 and chatgpt results are obtained via the openai api. each prompt is fed independently to avoid the effects of dialogue history. ## conclusion in this work, we comprehensively studied llms on zero-shot/few-shot mental health analysis and the impact of different emotion-enhanced prompts. we explored the potential of llms in explainable mental health analysis, by explaining their predictions via cot prompting. we developed a reliable annotation protocol for human evaluations of llm-generated explanations and benchmarked existing automatic evaluation metrics on the human annotations. experiments demonstrated that mental health analysis is still challenging for llms, but emotional information with proper prompt engineering can better trigger their ability. human evaluation results showed that chatgpt can generate human-level explanations for its decisions, and current automatic evaluation metrics need further improvement to properly evaluate explainable mental health analysis. chatgpt also bears limitations, including unstable predictions and inaccurate reasoning. in future work, we will explore domain-specific fine-tuning for llms to alleviate inaccurate reasoning problems. we will also extend the interpretable settings with llms to other research domains.