Optimizing Resume Data Extraction with Small Language Models: A Comparative Study on Efficiency and Privacy - Santos, Wagner Costa; Juiz, Gustavo Tomaello; Candido Junior, Arnaldo (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2025)
Resume data extraction remains a critical challenge in automated recruitment systems, where traditional approaches struggle with unstructured document formats while facing significant computational and privacy constraints. Large Language Models (LLMs) have shown promise for information extraction tasks but present substantial barriers including high computational costs and data privacy concerns when processing sensitive personal information. This study investigates the fine-tuning of Small Language Models (SLMs) as a viable alternative, specifically examining Phi-3 mini for extracting structured data from resumes. We employ advanced fine-tuning techniques including Parameter Efficient Fine-Tuning (PEFT) and Reparameterized Fine-Tuning with LoRA Quantization Adaptation (QLoRA) on a synthetic dataset of 2,100 resumes generated using privacy-preserving methods. Our comparative evaluation against GPT-4o demonstrates that the fine-tuned SLM achieves superior performance with 87.9% accuracy and 91.5 BLEU score, compared to GPT-4o’s 85.5% accuracy and 83.9 BLEU score, while significantly reducing computational costs. These findings establish SLMs as scalable and privacy-conscious alternatives for applications requiring nuanced information processing, offering comparable accuracy to LLMs with enhanced efficiency and data protection.