CG-CoT combines RAG and chain-of-thought prompting for Yoruba proverbs and reports higher cultural depth, but its accuracy result trails a baseline and no human evaluation supports the headline.
Psychological Assessments with Large Language Models: A Privacy-Focused and Cost-Effective Approach
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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.
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Culturally-Grounded Chain-of-Thought (CG-CoT):Enhancing LLM Performance on Culturally-Specific Tasks in Low-Resource Languages
CG-CoT combines RAG and chain-of-thought prompting for Yoruba proverbs and reports higher cultural depth, but its accuracy result trails a baseline and no human evaluation supports the headline.