Retrieval Augmented Classification for Confidential Documents
Unauthorized disclosure of confidential documents demands robust, low-leakage classification. In real work environments, there is a lot of inflow and outflow of documents. To continuously update knowledge, we propose a methodology for classifying confidential documents using Retrieval Augmented Classification (RAC). To confirm this effectiveness, we compare RAC and supervised fine tuning (FT) on the WikiLeaks US Diplomacy corpus under realistic sequence-length constraints. On balanced data, RAC matches FT. On unbalanced data, RAC is more stable while delivering comparable performance--about 96% Accuracy on both the original (unbalanced) and augmented (balanced) sets, and up to 94% F1 with...
| Authors | Yeseul E. Chang, Rahul Kailasa, Simon Shim et al. |
|---|---|
| Primary category | cs.CR |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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