CSIER-FM: A Chinese Sensitive Information Entity Recognition Framework with Few-Shot Learning and Multi-Instance Integration

The rapid growth of information technology has posed new challenges for recognizing sensitive information in Chinese text. Traditional rule-based, statistical, and machine learning methods face limitations in domain adaptability and Chinese semantic comprehension. Recently, large language models have offered new opportunities to address these challenges with their strong semantic understanding and transfer capabilities. Building on this, we propose the CSIER-FM, which is a Chinese sensitive information entity recognition framework that integrates prompt design, few-shot learning, parameter-efficient fine-tuning, and multi-instance integration. We design multiple prompt templates and incorporate a k-nearest neighbor (k-NN) sample selection strategy to optimize prompts and enhance the effectiveness of few-shot learning. In addition, we apply Low-Rank Adaptation (LoRA) to efficiently fine-tune the locally deployed Qwen2.5-7B model. Finally, a multi-instance integration mechanism is employed to allow different models to focus on specific entity categories, thereby reducing category confusion and enhancing overall F1-score. We evaluate the CSIER-FM on the Chinese ResumeNER dataset. The results demonstrate that fine-tuning the Qwen2.5-7B model raises its F1-score from 0.7106 to above 0.9518. With the addition of multi-instance integration, the F1-score further increased to 0.9553. The findings indicate that the CSIER-FM effectively integrates named entity recognition with Chinese sensitive information detection, enabling efficient recognition of multiple sensitive entity types in Chinese text.