Security Document Classification with a Fine-Tuned Local Large Language Model: Benchmark Data and an Open-Source System
Organizations that scan documents for sensitive information face a practical problem. Cloud services require data to be sent to external infrastructure, while rule-based tools often miss threats that depend on context. This study presents TorchSight, an open-source local system for security document classification built around a fine-tuned Qwen 3.5 27B model. The model was trained on 78,358 samples from 13 permissively licensed sources and GPT-4 synthetic data covering seven security categories and 51 subcategories. In the main evaluation on 1,000 documents, the model reached 95.0% category-level accuracy (95% confidence interval: 93.5-96.2). The tested commercial models scored 75.4-79.9%...
| Authors | Ivan Dobrovolskyi |
|---|---|
| Primary category | cs.CR |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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