Text Classification on Amazon (accuracy)
96.45AccuracyGNNAVI-GCN
Evaluation Results
| Method | Links | |
|---|---|---|
| GNNAVI-GCNBackbone=Llama2, k (training examples per class)=200, #Param=16.8M2024.02 | 96.45 | |
| FPFTBackbone=Llama2, k (training examples per class)=200, #Param=6.7B2024.02 | 96.12 | |
| GNNAVI-SAGEBackbone=Llama2, k (training examples per class)=200, #Param=33.6M2024.02 | 95.96 | |
| FPFTBackbone=Llama2, k (training examples per class)=5, #Param=6.7B2024.02 | 95.86 | |
| LORABackbone=Llama2, k (training examples per class)=200, #Param=4.2M2024.02 | 95.8 | |
| GNNAVI-SAGEBackbone=Llama2, k (training examples per class)=5, #Param=33.6M2024.02 | 95.66 | |
| ICLBackbone=Llama2, k (training examples per class)=02024.02 | 94.98 | |
| GNNAVI-GCNBackbone=Llama2, k (training examples per class)=5, #Param=16.8M2024.02 | 94 | |
| GNNAVI-SAGEBackbone=GPT2-XL, k (training examples per class)=200, #Param=5.1M2024.02 | 93.44 | |
| AdapterBackbone=GPT2-XL, k (training examples per class)=200, #Param=15.4M2024.02 | 92.3 | |
| ICLBackbone=Llama2, k (training examples per class)=52024.02 | 92.3 | |
| LORABackbone=Llama2, k (training examples per class)=5, #Param=4.2M2024.02 | 91.8 | |
| GCPannotation budget=20%2026.02 | 91.62 | |
| AdapterBackbone=GPT2-XL, k (training examples per class)=5, #Param=15.4M2024.02 | 91.45 | |
| GNNAVI-GCNBackbone=GPT2-XL, k (training examples per class)=5, #Param=2.6M2024.02 | 90.9 | |
| CALannotation budget=20%2026.02 | 90.54 | |
| ALPSannotation budget=20%2026.02 | 89.87 | |
| BADGEannotation budget=20%2026.02 | 89.81 | |
| Entropy Samplingannotation budget=20%2026.02 | 89.63 | |
| CoreSet (k-center)annotation budget=20%2026.02 | 89.32 | |
| Disagreement (Vote Entropy)annotation budget=20%2026.02 | 89.21 | |
| GNNAVI-GCNBackbone=GPT2-XL, k (training examples per class)=200, #Param=2.6M2024.02 | 89.2 | |
| Least Confidenceannotation budget=20%2026.02 | 88.94 | |
| LORABackbone=GPT2-XL, k (training examples per class)=5, #Param=2.5M2024.02 | 88.8 | |
| GNNAVI-SAGEBackbone=GPT2-XL, k (training examples per class)=5, #Param=5.1M2024.02 | 88.66 | |
| Way-DEWays=5, Shots=5-shot2026.02 | 87.4 | |
| Shot-DEWays=5, Shots=5-shot2026.02 | 86.9 | |
| Randomannotation budget=20%2026.02 | 86.6 | |
| MLADAWays=5, Shots=5-shot2026.02 | 86 | |
| SPCNetWays=5, Shots=5-shot2026.02 | 85.3 | |
| LDS-PNWays=5, Shots=5-shot2026.02 | 85.1 | |
| ProtoVerbWays=5, Shots=5-shot2026.02 | 84.7 | |
| TARTWays=5, Shots=5-shot2026.02 | 84.3 | |
| ContrastNetWays=5, Shots=5-shot2026.02 | 83.6 | |
| LORABackbone=GPT2-XL, k (training examples per class)=200, #Param=2.5M2024.02 | 82 | |
| LDS-PNWays=5, Shots=1-shot2026.02 | 81.8 | |
| DS-FSLWays=5, Shots=5-shot2026.02 | 81.1 | |
| LaSAMLWays=5, Shots=5-shot2026.02 | 79.1 | |
| SPCNetWays=5, Shots=1-shot2026.02 | 76.3 | |
| Shot-DEWays=5, Shots=1-shot2026.02 | 76.1 | |
| Way-DEWays=5, Shots=1-shot2026.02 | 76.1 | |
| FPFTBackbone=GPT2-XL, k (training examples per class)=200, #Param=1.6B2024.02 | 74.82 | |
| TARTWays=5, Shots=1-shot2026.02 | 73.7 | |
| ContrastNetWays=5, Shots=1-shot2026.02 | 73.5 | |
| FPFTBackbone=GPT2-XL, k (training examples per class)=5, #Param=1.6B2024.02 | 73 | |
| ProtoVerbWays=5, Shots=1-shot2026.02 | 72.4 | |
| MLADAWays=5, Shots=1-shot2026.02 | 68.4 | |
| DS-FSLWays=5, Shots=1-shot2026.02 | 62.6 | |
| LaSAMLWays=5, Shots=1-shot2026.02 | 62.2 | |
| PrefixBackbone=GPT2-XL, k (training examples per class)=5, #Param=6.1M2024.02 | 60 | |
| PrefixBackbone=GPT2-XL, k (training examples per class)=200, #Param=6.1M2024.02 | 59.8 | |
| ICLBackbone=GPT2-XL, k (training examples per class)=52024.02 | 53.67 | |
| ICLBackbone=GPT2-XL, k (training examples per class)=02024.02 | 53.32 | |
| PrefixBackbone=Llama2, k (training examples per class)=200, #Param=39.3M2024.02 | 52.28 | |
| PNWays=5, Shots=5-shot2026.02 | 52.1 | |
| AdapterBackbone=Llama2, k (training examples per class)=5, #Param=198M2024.02 | 49.45 | |
| AdapterBackbone=Llama2, k (training examples per class)=200, #Param=198M2024.02 | 49.45 | |
| PrefixBackbone=Llama2, k (training examples per class)=5, #Param=39.3M2024.02 | 49.36 | |
| MAMLWays=5, Shots=5-shot2026.02 | 47.1 | |
| INWays=5, Shots=5-shot2026.02 | 41.3 | |
| MAMLWays=5, Shots=1-shot2026.02 | 39.6 | |
| PNWays=5, Shots=1-shot2026.02 | 37.6 | |
| INWays=5, Shots=1-shot2026.02 | 34.9 |