A Dual-Prompting for Interpretable Mental Health Language Models (2024)

Abstract: Despite the increasing demand for AI-based mental health monitoring tools, their practical utility for clinicians is limited by the lack of interpretability. The CLPsych 2024 Shared Task (Chim et al., 2024) aims to enhance the interpretability of Large Language Models (LLMs), particularly in mental health analysis, by providing evidence of suicidality through linguistic content. We propose a dual-prompting approach: (i) Knowledge-aware evidence extraction by leveraging the expert identity and a suicide dictionary with a mental health-specific LLM; and (ii) Evidence summarization by employing an LLM-based consistency evaluator. Comprehensive experiments demonstrate the effectiveness of combining domain-specific information, revealing performance improvements and the approach’s potential to aid clinicians in assessing mental state progression.

## introduction the global healthcare system faces significant challenges from mental health conditions such as depression and suicidal ideation @xcite , emphasizing the need for an advanced monitoring system for early intervention @xcite . in response, nlp researchers have paid attention to identifying mental states, often leveraging social media data @xcite @xcite . notably, the most recent development involves the application of large language models (llms), which have demonstrated robust capabilities in general language processing in mental health analysis @xcite . @xcite conducted a comparison of chatgpt's zero-shot capability in identifying suicide and depression, contrasting it with previous methods that relied on previous pretrained language models (plms). furthermore, lamichhane (2023) evaluated chatgpt's effectiveness in recognizing stress, depression, and suicide, emphasizing its strong grasp of language in texts related to mental health. however, these studies have focused on identifying mental health status through a black box model, posing a challenge in interpreting the rationale behind their outcomes @xcite . accordingly, efforts have been made to enhance the interpretability of mental health analysis, such as guiding llms to emphasize emotional cues @xcite and developing open-source llms by training them with data from mental health-related social media @xcite . nevertheless, a lack of reliability still remains; recent llms are often unreliable or inconsistent @xcite , potentially due to the lack of mental health-related knowledge @xcite . this problem has significantly delayed the practical use of llms in clinical settings @xcite . to address this issue, the clpsych 2024 shared task @xcite introduces the challenge of utilizing open-source llms to enhance their interpretability in mental health analysis, specifically focusing on detecting suicidality through linguistic content in social media data. particularly, the shared task includes two subtasks: (i) task a requires finding key phrases from each post to support suicide risk, and (ii) task b aims to provide a summary of evidence related to the user's suicide risk across multiple posts. in this paper, we design an enhanced prompt for the extraction task (task a) by assigning an expert identity, enabling llms to function as an expected agent @xcite , and leveraging a suicide dictionary @xcite to capture suicide-related context. here, we utilize mental health-specific llm, mentallama @xcite . for the summarization task (task b), we employ a consistency evaluator @xcite to the extensive experiments illustrate that combining domain-specific information with few-shot learning enhances the extraction of evidence, resulting in an improvement in recall from 91.0% to 92.2%. additionally, our findings indicate that an llm trained with general datasets is more effective in mitigating hallucination in summarization tasks than a domain-specific llm. we believe our approach can support clinicians in assessing mental state progression. ## methodology our aim is (i) to extract evidence supporting the user's suicide risk from each post and (ii) to summarize all the evidence across multiple posts. to this end, we design two prompting strategies to instruct llms for trustworthy reasoning in mental health analysis. these strategies include knowledgeaware evidence extraction ( §2.1) and evidence summarization with an llm-based consistency 1 , and the full text of each prompt is available in appendix a. ## results & analysis to demonstrate the effectiveness of the proposed method, we compare its performance with various approaches and conduct the case study where our proposed approach performs better. note that due to the absence of ground truth from the organizer, quantitative analysis was limited, leading us to focus on qualitative analysis instead. additionally, we manually paraphrase any examples from the data to preserve user anonymity. ## conclusion in this study, we introduced promising prompting strategies that can provide evidence supporting suicide risk levels on social media data. we enhanced the llm interpretability by incorporating domainspecific elements like assigning a psychiatrist identity and combining a suicide word. additionally, we improved the consistency in summarization by using a consistency evaluator with multiple candidates. the proposed dual-prompting approach provides reliable reasoning, making it suitable for monitoring mental health-related risks.