Fine-Tuning Over Architectural Complexity: Broad-Coverage PII Detection on PIIBench with DeBERTa
arXiv:2605.25816v1 Announce Type: new Abstract: Personally identifiable information (PII) detection systems are frequently trained within narrow source or domain boundaries, limiting coverage when deployed on heterogeneous text. We study model fine-tuning on a corrected multi-source PIIBench preparation spanning 82 retained entity types across ten source datasets. We evaluate three DeBERTa-based approaches: direct token classification fine-tuning, a source-conditioned hierarchical model (SC+H), and a three-phase curriculum extension (SC+H+Curr). Against eight published comparator systems on a
Ongoing advancements in natural language processing and the increasing need for robust data privacy solutions are driving focused research into broad-coverage PII detection.
Improved PII detection systems are critical for compliance, data security, and the responsible deployment of AI applications across diverse textual datasets.
The research suggests a more effective approach to training PII detection models, potentially leading to more accurate and widely applicable systems for safeguarding sensitive information.
- · SaaS providers
- · Data privacy and compliance sectors
- · Healthcare and finance industries
- · Cybercriminals
- · Organizations with lax data handling practices
More effective PII detection tools become available, reducing data breach risks.
Increased trust in AI systems due to better handling of sensitive personal data.
New regulatory frameworks may emerge, leveraging advanced PII detection capabilities as a baseline for data protection requirements.
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Read at arXiv cs.CL