Moving Beyond ChatGPT: Local Large Language Models (LLMs) and the Secure Analysis of Confidential Unstructured Text Data in Social Work Research

Abstract

Purpose: Large language models (LLMs) have demonstrated remarkable abilities in natural language tasks. However, their use in social work research is limited by confidentiality and security concerns when processing sensitive data. This study addresses these challenges by evaluating the performance of local LLMs (LocalLLMs) in classifying and extracting substance-related problems from unstructured child welfare investigation summaries. LocalLLMs allow researchers to analyze data on their own computers without transmitting information to external servers for processing. Methods: Four state-of-the-art LocalLLMs—Mistral-7b, Mixtral-8 × 7b, LLama3-8b, and Llama3-70b—were tested using zero-shot prompting on 2,956 manually coded summaries. Results: The LocalLLMs achieved exceptional results comparable to human experts in classification and extraction, demonstrating their potential to unlock valuable insights from confidential, unstructured child welfare data. Conclusions: This study highlights the feasibility of using LocalLLMs to efficiently analyze large amounts of textual data while addressing the confidentiality issues associated with proprietary LLMs.

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