Not Every Change Is Necessary: Recoverable Drift in Large Language Model Unlearning

arXiv:2610.11915 · cs.CL · Submitted 2026-10-08 · Read on arXiv

cs.CL

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Transformer — 10× in this paper · explained in 2 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Benchmark — 6× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Adaptation — 4× in this paper · explained in 1 episode
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Machine Unlearning — 3× in this paper · explained in 2 episodes
• Continual Learning — 2× in this paper · explained in 2 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Fisher Information — 2× in this paper · explained in 3 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Steering — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Activation Steering — 1× in this paper · explained in 4 episodes
• Adversarial Prompting — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Approximate Unlearning — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Error Decomposition — 1× in this paper · explained in 3 episodes
• KL Regularization — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Online Reinforcement Learning — 1× in this paper · explained in 2 episodes
• RAG — 1× in this paper · explained in 3 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Semantic Drift — 1× in this paper · explained in 3 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Two-Stage Framework — 1× in this paper · explained in 2 episodes

Sources

• Who's Harry Potter? Approximate Unlearning in LLMs
• The Llama 3 Herd of Models
• Measuring Massive Multitask Language Understanding
• TOFU: A Task of Fictitious Unlearning for LLMs
• Qwen3 Technical Report
• UniErase: Towards Balanced and Precise Unlearning in Language Models
• Qwen3Guard Technical Report

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