Exploring Instructive Prompts for Large Language Models in the Extraction of Evidence for Supporting Assigned Suicidal Risk Levels (2024)

Abstract: Monitoring and predicting the expression of suicidal risk in individuals’ social media posts is a central focus in clinical NLP. Yet, existing approaches frequently lack a crucial explainability component necessary for extracting evidence related to an individual’s mental health state. We describe the CSIRO Data61 team’s evidence extraction system submitted to the CLPsych 2024 shared task. The task aims to investigate the zero-shot capabilities of open-source LLM in extracting evidence regarding an individual’s assigned suicide risk level from social media discourse. The results are assessed against ground truth evidence annotated by psychological experts, with an achieved recall-oriented BERTScore of 0.919. Our findings suggest that LLMs showcase strong feasibility in the extraction of information supporting the evaluation of suicidal risk in social media discourse. Opportunities for refinement exist, notably in crafting concise and effective instructions to guide the extraction process.

## introduction the intersection between nlp and mental health research has provided valuable insights, uncovering the diagnostic potential inherent in language @xcite . previous research has primarily concentrated on static classifications of individuals' social media posts, with studies, for example, focusing on predicting the level of suicide risk within social media posts @xcite @xcite and tracking changes in emotion over time @xcite @xcite . despite these advancements, the increasing reliance on computational models in mental health assessments unveils a prominent gap -the lack of an essential explainability component. this absence is critical for the nuanced extraction of evidence that explains an individual's mental health state. this deficiency assumes significance in supporting practitioners' decision-making as they navigate the intricacies of mental health diagnostics. in response to this problem, a shared task is organised as part of the clpsych 2024 workshop @xcite . in our participation, we investigate the application of an open-source large language model (llm), namely llama-2 @xcite within a zero-shot learning framework. the principal objective is to systematically extract text spans that can be treated as evidence of an individual's assigned suicide risk level from their social media posts. beyond the mere evaluation of llm viability, we assume a proactive stance, aiming to formulate instructive prompts that guide the model in extracting accurate and semantically rich evidence. we use a sub-sample of the university of maryland reddit suicidality dataset, version 2, which includes 125 randomly selected reddit users and their r/suicidew atch posts @xcite , provided by the task organisers. the suicide risk levels of these users are annotated by psychologists. the robustness and validity of our findings are ensured through evaluation against ground truth evidence annotated by domain experts, employing bertscore @xcite . overall, we found instructing llm with factor-oriented and risk-levelspecific prompts achieved the best recall-oriented bertscore of 0.919 among our experimented approaches. ## dataset a sub-sample of 125 users and their posts on the r/suicidew atch subreddit was selected from the university of maryland reddit suicidality dataset (umd subset) @xcite . each user in the subset creates 1.3 posts on average, with a maximum of three posts. psychology experts conducted annotations of the suicidal risk level for each user, classifying them as low, moderate, or high (or severe) risk through a comprehensive review of all posts associated with a particular user. note that the risk level annotation is performed at the user level rather than the post level. specifically, each user receives an annotation based on the highest risk level expressed throughout their entire collection of posts. to provide clarity, in instances where a user conveys high-risk suicidal thoughts in an initial post followed by expressing low risk in a subsequent post, the user's annotation reflects the highest risk level. we utilise the provided umd subset, consisting of 125 users, to investigate the application of llms for evidence extraction using zero-shot learning. for development, we randomly select nine users from the broader umd dataset, where the suicide risk levels are annotated through crowd-sourcing, focusing on posts from r/suicidew atch. this ensures their distinction from the 125 users in the provided umd subset. ## method we design three approaches: (1) a baseline, (2) a factor-oriented, and (3) a risk level & factororiented approach. each method varies in the design of the prompt in the zero-shot learning setting. ## evaluation metrics the evaluation was conducted by the shared task organisers using bertscore @xcite . assume g is a set of 4 gold highlights @xmath0, g 2 , g 3 , g 4 } and h is a set of 2 submitted highlights @xmath1, h 2 }. then, the evaluation metrics are: the overall submission-level score is the mean across all test users. ## results the baseline demonstrated excellent precision (0.918) in extracting evidence. this observation suggests that while the llm may not comprehensively grasp the causative factors for evaluating suicide risk levels in context, and may fail to cover all aspects, it has embedded enough knowledge to accurately identify relevant context. it also achieved the best weighted-recall of 0.740 among the experimented approaches, indicating its extraction length is closer to the human annotation compared to the instruction that explicitly covers the risk factors as guidance. upon comparing the rf-oriented approach to the factor-oriented approach, we noticed that refining instructions to the llm for conciseness led to improved performance (+0.007 in recall; +0.002 in precision; +0.022 in weighted-recall) in evidence extraction. specifically, when excluding the extraction of evidence for low-risk annotations, the rf-oriented approach, with instructions tailored for different risk levels, demonstrated the ability to extract shorter context and achieved better weighted-recall. ## conclusions we investigated three approaches with varying levels of instruction detail to guide llms in extracting evidence related to users exhibiting low, moderate, or high suicide risk levels. all approaches demonstrated strong effectiveness, with the baseline excelling in precision for shorter text pieces. however, the factor-oriented and rf-oriented approaches, equipped with detailed instructions covering diverse mental health factors tailored to different risk levels, proved more effective in capturing comprehensive evidence, with the rf-oriented approach performing the best. our findings highlight the robust feasibility of llms in extracting information supporting the evaluation of suicidal risk in social media discourse. there is room for improvement by creating clear and effective instructions to steer the extraction process. this could involve adapting existing manual annotation guidelines for evidence extraction into instructive prompts. ad- in future, we will conduct a more comprehensive qualitative analysis. we aim to refine the instructional prompts given to the model, adapting existing manual annotation guidelines to ensure clearer and more effective guidance. we will explore the integration of contextual information, aiming to enhance the model's ability to capture broader situational cues for improved risk assessment. addressing the auto-correction behavior, especially in terms of spelling errors, will be a priority, involving fine-tuning the model or implementing postprocessing steps to preserve the original expressions in extracted text.