arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2402.03435cs.CLcs.AIcs.CY
收藏

基于大语言模型的心理评估:一种注重隐私且经济高效的方法

Psychological Assessments with Large Language Models: A Privacy-Focused and Cost-Effective Approach

Harvard-Smithsonian Center for Astrophysics(哈佛-史密森尼天体物理中心);Laboratoire de Recherche en Neuroimagerie(神经成像研究实验室);University Hospital (CHUV)(洛桑大学医院);University of Lausanne (UNIL)(洛桑大学)

Sergi Blanco-Cuaresma

AI总结:

本研究利用开源大语言模型分析Reddit文本,通过提示和语法引导实现自杀风险评估与摘要,兼顾隐私与低成本,在CLPsych 2024任务中表现优异。

摘要:

本研究探索使用大语言模型(LLMs)分析Reddit用户的文本评论,旨在实现两个主要目标:第一,识别支持预定义的自杀风险心理评估的关键摘录;第二,总结材料以证实预先分配的自杀风险等级。本研究仅限于使用可在本地运行的开源大语言模型,从而增强数据隐私。此外,本研究优先考虑计算需求较低的模型,使个人和计算预算有限的机构都能使用。所实施的策略仅依赖于精心设计的提示和语法来引导大语言模型的文本生成。尽管方法简单,评估指标显示出优异的结果,使其成为一种有价值的注重隐私且经济高效的方法。本研究是计算语言学和临床心理学(CLPsych)2024年共享任务的一部分。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑