Team ISM at CLP sych 2024: Extracting Evidence of Suicide Risk from R eddit Posts with Knowledge Self-Generation and Output Refinement using A Large Language Model (2024)

Abstract: This paper presents our approach to the CLPsych 2024 shared task: utilizing large language models (LLMs) for finding supporting evidence about an individual’s suicide risk level in Reddit posts. Our framework is constructed around an LLM with knowledge self-generation and output refinement. The knowledge self-generation process produces task-related knowledge which is generated by the LLM and leads to accurate risk predictions. The output refinement process, later, with the selected best set of LLM-generated knowledge, refines the outputs by prompting the LLM repeatedly with different knowledge instances interchangeably. We achieved highly competitive results comparing to the top-performance participants with our official recall of 93.5%, recall–precision harmonic-mean of 92.3%, and mean consistency of 96.1%.

## introduction in the unprecedented rapid evolution of large language models (llms), the ninth workshop on computational linguistics and clinical psychology (clpsych 2024) introduced the shared task of utilizing llms for finding supporting evidence about an individual's suicide risk level in reddit posts @xcite . it is evident that recent work on llms suggest their potential applications on clinical tasks such as information extraction @xcite ) and question answering @xcite . the clpsych 2024 shared task uses the same reddit dataset as the clpsych 2019 shared task @xcite which consisted of reddit posts and annotated users' suicide risk labels at 4 levels: no risk, low risk, moderate risk, and high (severe) risk. the annotations were performed by both experts and crowd-source workers. the clpsych 2024 shared task focuses on the expert annotations of users and posts on the subreddit 'r/suicidewatch', where users are annotated with risk labels at 3 levels: low risk, moderate risk or high risk. from the user posts and annotations, the task's goal is to find supporting evidence in the form of post highlights and a summarized evidence given a user. it is important to note that the ground-truth evidence is not available for the task participants, so no direct optimization using ground-truth evidence is possible. task definition. formally, given a user i who was assessed with either low risk, moderate risk, or high risk of committing suicide, a set of their reddit posts v i , and their suicide risk assessment a i in 3 risk levels (low risk, moderate risk, high risk), the goal is to identify the evidence supporting the assessment a i in the form of post highlights and a summary: {h i , s i }. overview of our approach. our framework is constructed around an llm with knowledge self-generation and output refinement. inspired by medprompt @xcite where using llmgenerated knowledge of solving a task as a part of task prompting can help boost the performance of such task, we ask the llm to respond with its knowledge of how to make suicide risk assessment and find supporting evidence from social media posts. the best set of llm-generated knowledge is selected based on the prediction accuracy. now we have output candidates from potential knowledge leading to accurate predictions. we design an output refinement process to aggregate and refine the output candidates to obtain the final output. in the refinement process, the llm is prompted with interchanging knowledge on the same input repeatedly, so when finished, we obtained an output with more agreement among different knowledge instances. similar to our idea of output refinement, @xcite proposed a self-refinement process where the output is put back to the same llm, albeit the same conversation, to get feedback and refined output. our approach, instead of going in-depth with one conversation, collectively refines the output under various input knowledge. ## results & discussions the results are obtained with the metrics briefly described below. please refer to the organizer's paper @xcite for the details of the evaluation metrics. in stage 1, we acquired 320 knowledge responses, averaging 55.4% accuracy in risk prediction, with a peak accuracy of 72.8%. an example of the responses is shown in appendix a. in stage 2, we observed convergence with stopped iteration τ not going over 5 and having an average of 1.4 for run 2 and 1.9 for run 3. the refinement process helps reduce the length of extracted highlights from 53% to 32-33%, which leads to a big improvement of weighted recall despite the cost of lower recall ( ## conclusion in the concept of the clpsych 2024 shared task, we have constructed a framework for extracting evidence of suicide risk from reddit posts with knowledge-self generation and output refinement using an llm. we achieved competitive results among the top participants. our future work needs to focus on improving output refinement, and tackling consistency problems in matching supporting evidence with risk prediction.