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

TL;DR

This paper presents a privacy-preserving, cost-effective method using open-source large language models to analyze Reddit comments for psychological assessment of suicidal risk, emphasizing simplicity and accessibility.

Contribution

It introduces a novel approach leveraging open-source LLMs with minimal prompts and grammar for sensitive psychological assessments, enhancing privacy and reducing computational costs.

Findings

Achieved outstanding evaluation metrics in suicidal risk assessment

Demonstrated effectiveness with low-resource, locally run models

Validated approach as a practical tool for clinical and research use

Abstract

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.