KUDA: Knowledge Unlearning by Deviating Representation for Large Language Models

arXiv:2602.19275 · cs.CR · Submitted 2026-02-22 · Read on arXiv

cs.CR

Submitted: 2026-02-22

Updated: 2026-09-17

Terminology

Null-Space Projection — 17× in this paper
Alignment — 15× in this paper · explained in 1 episode
Benchmark — 15× in this paper · explained in 2 episodes
Fine-Tuning — 10× in this paper · explained in 5 episodes
Accuracy — 8× in this paper · explained in 1 episode
Aggregation — 8× in this paper · explained in 1 episode
Machine Unlearning — 8× in this paper · explained in 2 episodes
Transformer — 8× in this paper · explained in 2 episodes
Computational overhead — 5× in this paper · explained in 2 episodes
Core — 5× in this paper · explained in 2 episodes
Decoupling — 5× in this paper · explained in 2 episodes
Entanglement — 5× in this paper · explained in 3 episodes
Hit Ratio — 4× in this paper
Loss Function — 4× in this paper · explained in 2 episodes
Reinforcement Learning — 4× in this paper · explained in 4 episodes
Robustness — 4× in this paper · explained in 5 episodes
AUC — 3× in this paper · explained in 2 episodes
Activation Function — 3× in this paper · explained in 1 episode
Consistency — 3× in this paper · explained in 3 episodes
Cosine Similarity — 3× in this paper · explained in 3 episodes
Knowledge Removal Degree — 3× in this paper
Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
Ablation — 2× in this paper · explained in 1 episode
Adversarial Attacks — 2× in this paper · explained in 1 episode
Alignment Techniques — 2× in this paper · explained in 1 episode
Continual Learning — 2× in this paper · explained in 2 episodes
Generalizability — 2× in this paper · explained in 6 episodes
Generalization — 2× in this paper · explained in 13 episodes
Interpretability — 2× in this paper · explained in 15 episodes
Proximal Policy Optimization — 2× in this paper
Regularization — 2× in this paper · explained in 3 episodes
Supervised Fine-Tuning (SFT) — 2× in this paper · explained in 8 episodes
Supervised Fine-tuning — 2× in this paper
Transferability — 2× in this paper · explained in 4 episodes
Ablation study — 1× in this paper · explained in 1 episode
Adaptability — 1× in this paper · explained in 1 episode
Approximate Unlearning — 1× in this paper · explained in 1 episode
Associative Memory — 1× in this paper · explained in 2 episodes
Benchmarking — 1× in this paper · explained in 3 episodes
Calibration — 1× in this paper · explained in 7 episodes
Distillation — 1× in this paper · explained in 2 episodes
Downstream Tasks — 1× in this paper · explained in 2 episodes
Embeddings — 1× in this paper · explained in 2 episodes
F1 Score — 1× in this paper · explained in 3 episodes
Foundation Model — 1× in this paper · explained in 3 episodes
Foundation Models — 1× in this paper · explained in 7 episodes
Generative AI — 1× in this paper · explained in 2 episodes
Knowledge Distillation — 1× in this paper · explained in 11 episodes
Latent Representation — 1× in this paper · explained in 2 episodes
Membership Inference Attacks — 1× in this paper
Proximal Policy Optimization (PPO) — 1× in this paper · explained in 4 episodes
Pruning — 1× in this paper · explained in 4 episodes
Singular Value Decomposition (SVD) — 1× in this paper · explained in 2 episodes
Sparsity — 1× in this paper · explained in 4 episodes
Steering — 1× in this paper · explained in 2 episodes
Trace — 1× in this paper · explained in 2 episodes
Unified Framework — 1× in this paper · explained in 5 episodes
Verification — 1× in this paper · explained in 2 episodes

Terminology

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