This study explores the use of Large Language Models (LLMs) to analyze text comments from Reddit users, aiming to achieve two primary objectives: firstly, to pinpoint critical excerpts that support a predefined psychological assessment of suicidal risk; and secondly, to summarize the material to substantiate the preassigned suicidal risk level. The work is circumscribed to the use of "open-source" LLMs that can be run locally, thereby enhancing data privacy. Furthermore, it prioritizes models with low computational requirements, making it accessible to both individuals and institutions operating on limited computing budgets. The implemented strategy only relies on a carefully crafted prompt and a grammar to guide the LLM's text completion. Despite its simplicity, the evaluation metrics show outstanding results, making it a valuable privacy-focused and cost-effective approach. This work is part of the Computational Linguistics and Clinical Psychology (CLPsych) 2024 shared task.

翻译：本研究探索利用大语言模型分析Reddit用户的文本评论，旨在实现两个主要目标：首先，识别支持预定义自杀风险心理评估的关键片段；其次，总结材料以佐证预先分配的自杀风险等级。这项工作限定使用可在本地运行的开源大语言模型，从而增强数据隐私保护。此外，本研究优先选择计算需求较低的模型，使其能够被计算预算有限的个人和机构使用。所实施的策略仅依赖于精心设计的提示词和语法规则来引导大语言模型的文本生成。尽管方法简单，但评估指标展现出卓越效果，使其成为一种兼顾隐私保护与经济性的可行方案。本工作隶属于计算语言学与临床心理学2024年联合任务。