A Classifier That Teaches Itself: Self-Improving, Frozen-gate Training (SIFT) for Dynamic Document Classification

arXiv:2607.18358 · cs.CL, cs.LG · Submitted 2026-07-20 · Read on arXiv

cs.CL, cs.LG

Submitted: 2026-07-20

Updated: 2026-09-01

Terminology

• Accuracy — 11× in this paper · explained in 1 episode
• LLM judge — 9× in this paper · explained in 2 episodes
• Active Learning — 7× in this paper · explained in 6 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Concept Drift — 1× in this paper · explained in 4 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes

Sources

• Enhancing Text Classification through LLM-Driven Active Learning and Human Annotation
• FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance
• Applying LLMs to Active Learning: Towards Cost-Efficient Cross-Task Text Classification without Manually Labeled Data

Related papers

• Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
• Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
• Subliminal Steering: Stronger Encoding of Hidden Signals
• MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
• The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
• Untangling the Mechanisms of Misleading Context in Medical Question Answering