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Machine Unlearning on TOFU Forget01 (1% authors)

0.99Forget Quality (Rouge-L)Original
-0.01880.24310.5050.7669Sep 29, 2025

Evaluation results

MethodLinks
OriginalBackbone=Llama2-7B2025.090.990.5100.980.470.940.620.890.550.63
OriginalBackbone=Llama2-7B2025.090.990.5100.980.470.940.620.890.550.63
OriginalBackbone=Llama2-7B2025.090.990.5100.980.470.940.620.890.550.63
DPOBackbone=Phi-1.5B, Training Type=Full Fine-tuning2025.090.960.490.00010.910.480.420.450.760.510.52
KLBackbone=Llama2-7B, Tuning Strategy=Full Fine-tuning2025.090.950.550.00010.980.470.940.620.890.550.63
DPOBackbone=Llama2-7B, Tuning Strategy=Full Fine-tuning2025.090.950.550.00010.980.470.930.620.890.550.63
NPOBackbone=Llama2-7B, Tuning Strategy=Full Fine-tuning2025.090.950.550.00010.980.470.930.620.90.550.63
GABackbone=Llama2-7B, Tuning Strategy=Full Fine-tuning2025.090.950.550.00010.980.470.930.620.890.550.63
GDBackbone=Llama2-7B, Tuning Strategy=Full Fine-tuning2025.090.950.550.00010.980.470.930.620.90.550.63
IHLBackbone=Llama2-7B, Tuning Strategy=Full Fine-tuning2025.090.950.550.00010.980.470.940.620.90.550.63
KLBackbone=Llama2-7B, Method Class=Full Fine-tuning2025.090.950.550.00010.980.470.940.620.90.560.63
DPOBackbone=Llama2-7B, Method Class=Full Fine-tuning2025.090.950.560.00010.980.470.930.620.890.550.63
NPOBackbone=Llama2-7B, Method Class=Full Fine-tuning2025.090.950.550.00010.980.470.930.620.890.560.63
GABackbone=Llama2-7B, Method Class=Full Fine-tuning2025.090.950.550.00010.980.470.940.620.890.560.63
GDBackbone=Llama2-7B, Method Class=Full Fine-tuning2025.090.950.550.00010.980.470.940.620.890.550.63
IHLBackbone=Llama2-7B, Method Class=Full Fine-tuning2025.090.950.550.00010.980.470.940.620.90.550.63
KLBackbone=Llama2-7B, Training Strategy=Full Fine-tuning2025.090.950.550.00010.980.470.940.620.890.550.63
DPOBackbone=Llama2-7B, Training Strategy=Full Fine-tuning2025.090.950.550.00010.980.470.930.620.890.550.63
NPOBackbone=Llama2-7B, Training Strategy=Full Fine-tuning2025.090.950.550.00010.980.470.930.620.90.550.63
GABackbone=Llama2-7B, Training Strategy=Full Fine-tuning2025.090.950.550.00010.980.470.930.620.890.550.63
GDBackbone=Llama2-7B, Training Strategy=Full Fine-tuning2025.090.950.550.00010.980.470.930.620.90.550.63
IHLBackbone=Llama2-7B, Training Strategy=Full Fine-tuning2025.090.950.550.00010.980.470.940.620.90.550.63
OriginalBackbone=Phi-1.5B, Method Category=Baseline2025.090.930.4800.920.480.410.450.750.50.52
GDBackbone=Phi-1.5B, Training Type=Full Fine-tuning2025.090.880.480.00010.920.490.40.450.760.50.52
IHLBackbone=Phi-1.5B, Training Type=Full Fine-tuning2025.090.880.480.00010.910.490.420.450.760.50.52
KLBackbone=Phi-1.5B, Training Type=Full Fine-tuning2025.090.840.4800.910.480.460.450.750.490.53
GABackbone=Phi-1.5B, Training Type=Full Fine-tuning2025.090.840.480.00010.910.480.440.450.750.490.52
NPOBackbone=Phi-1.5B, Training Type=Full Fine-tuning2025.090.820.4800.910.480.440.450.760.490.52
LoKUBackbone=Llama2-7B, Method Class=Parameter-Efficient, LoRA Rank=42025.090.690.550.00020.980.470.930.60.890.540.62
LoKUBackbone=Llama2-7B, Tuning Strategy=Parameter-Efficient, LoRA Rank=rank-322025.090.680.550.00020.980.470.930.60.90.540.62
LoKUBackbone=Llama2-7B, Training Strategy=Parameter-Efficient, LoRA Rank=rank-162025.090.680.550.00020.980.470.930.60.90.540.62
LoKUBackbone=Phi-1.5B, LoRA Rank=16, Training Type=Parameter-Efficient2025.090.440.550.00170.750.490.370.470.720.530.51
Retain90Backbone=Phi-1.5B, Method Category=Baseline2025.090.430.6310.910.480.430.450.760.490.52
Retain90Backbone=Llama2-7B2025.090.410.6610.980.470.920.610.880.550.63
Retain90Backbone=Llama2-7B, Retraining Condition=90% of data2025.090.410.6610.980.470.920.610.880.550.63
GD+SineBackbone=Llama2-7B, Tuning Strategy=Parameter-Efficient, LoRA Rank=rank-322025.090.40.510.920.980.440.940.620.90.60.68
Retain90Backbone=Llama2-7B2025.090.40.6710.980.470.920.610.880.550.63
OURS (GD+Sine)Backbone=Llama2-7B, Method Class=Parameter-Efficient, LoRA Rank=42025.090.40.50.090.980.480.940.620.90.60.63
GD+SineBackbone=Llama2-7B, Training Strategy=Parameter-Efficient, LoRA Rank=rank-162025.090.40.510.920.980.450.940.620.90.60.68
GD+SineBackbone=Phi-1.5B, LoRA Rank=16, Training Type=Parameter-Efficient2025.090.350.460.9430.930.480.410.460.770.490.52
GD+FILABackbone=Llama2-7B, Tuning Strategy=Parameter-Efficient, LoRA Rank=rank-322025.090.030.8700.030.1200.210.010.230
GA+FILABackbone=Llama2-7B, Method Class=Parameter-Efficient, LoRA Rank=42025.090.030.8700.040.120.010.220.010.250
GD+FILABackbone=Llama2-7B, Method Class=Parameter-Efficient, LoRA Rank=42025.090.030.8700.040.1300.210.020.240
GA+FILABackbone=Phi-1.5B, LoRA Rank=16, Training Type=Parameter-Efficient2025.090.030.640.01140.010.190.010.270.010.290
GD+FILABackbone=Phi-1.5B, LoRA Rank=16, Training Type=Parameter-Efficient2025.090.030.60.02440.020.1900.270.010.360
GD+FILABackbone=Llama2-7B, Training Strategy=Parameter-Efficient, LoRA Rank=rank-162025.090.030.8700.030.1200.210.010.230
GA+FILABackbone=Llama2-7B, Tuning Strategy=Parameter-Efficient, LoRA Rank=rank-322025.090.020.880.00010.030.130.010.210.020.240
GA+FILABackbone=Llama2-7B, Training Strategy=Parameter-Efficient, LoRA Rank=rank-162025.090.020.880.00010.030.130.010.210.020.240