SIFT: A Self-Improving Classifier That Reduces Labeling Costs and Prevents Regression
Researchers introduce SIFT (Self-Improving, Frozen-gate Training), a dynamic document classification system that combines a cost-effective CPU-based pipeline (SPLADE + LightGBM) with selective escalation of uncertain cases to a large language model (LLM) judge. The LLM's decisions are incorporated into the training data, enabling the system to improve accuracy and reduce the need for manual labeling over time. SIFT employs a frozen-gate mechanism, including F1 regression checks and a golden test set, to prevent silent performance regressions during autonomous retraining.
Why it matters: SIFT offers a novel approach to reducing the high costs and risks associated with manual labeling and autonomous retraining in enterprise document classification.
Full story at: arXiv Computation and Language ↗