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Models/T-Few

T-Few

Reported on 36 benchmarks across 3 tasks · 1 paper · 21 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing24 results

Text ClassificationonRAFT
Over· 2022-05-11
0.95
SOTA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Text ClassificationonRAFT
Avg· 2022-05-11
0.758
SOTA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Text ClassificationonRAFT
B77· 2022-05-11
0.695
SOTA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Text ClassificationonRAFT
OSE· 2022-05-11
0.676
SOTA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Text ClassificationonRAFT
SOT· 2022-05-11
0.915
SOTA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Text ClassificationonRAFT
TAI· 2022-05-11
0.736
SOTA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Text ClassificationonRAFT
ToS· 2022-05-11
0.75
SOTA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Few-Shot Text ClassificationonRAFT
Over· 2022-05-11
0.95
SOTA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Few-Shot Text ClassificationonRAFT
Avg· 2022-05-11
0.758
SOTA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Few-Shot Text ClassificationonRAFT
B77· 2022-05-11
0.695
SOTA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Few-Shot Text ClassificationonRAFT
OSE· 2022-05-11
0.676
SOTA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Few-Shot Text ClassificationonRAFT
SOT· 2022-05-11
0.915
SOTA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Few-Shot Text ClassificationonRAFT
TAI· 2022-05-11
0.736
SOTA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Few-Shot Text ClassificationonRAFT
ToS· 2022-05-11
0.75
SOTA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Text ClassificationonRAFT
ADE· 2022-05-11
0.804
best: 0.83 (Human (crowdsourced))
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Text ClassificationonRAFT
NIS· 2022-05-11
0.833
best: 0.857 (Human (crowdsourced))
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Text ClassificationonRAFT
SRI· 2022-05-11
0.508
best: 0.516 (GPT-3)
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Text ClassificationonRAFT
TC· 2022-05-11
0.879
best: 0.897 (Human (crowdsourced))
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Text ClassificationonRAFT
TEH· 2022-05-11
0.586
best: 0.722 (Human (crowdsourced))
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Few-Shot Text ClassificationonRAFT
ADE· 2022-05-11
0.804
best: 0.83 (Human (crowdsourced))
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Few-Shot Text ClassificationonRAFT
NIS· 2022-05-11
0.833
best: 0.857 (Human (crowdsourced))
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Few-Shot Text ClassificationonRAFT
SRI· 2022-05-11
0.508
best: 0.516 (GPT-3)
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Few-Shot Text ClassificationonRAFT
TC· 2022-05-11
0.879
best: 0.897 (Human (crowdsourced))
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
Few-Shot Text ClassificationonRAFT
TEH· 2022-05-11
0.586
best: 0.722 (Human (crowdsourced))
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638

Methodology12 results

ClassificationonRAFT
Over· 2022-05-11
0.95
SOTA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
ClassificationonRAFT
Avg· 2022-05-11
0.758
SOTA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
ClassificationonRAFT
B77· 2022-05-11
0.695
SOTA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
ClassificationonRAFT
OSE· 2022-05-11
0.676
SOTA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
ClassificationonRAFT
SOT· 2022-05-11
0.915
SOTA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
ClassificationonRAFT
TAI· 2022-05-11
0.736
SOTA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
ClassificationonRAFT
ToS· 2022-05-11
0.75
SOTA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
ClassificationonRAFT
ADE· 2022-05-11
0.804
best: 0.83 (Human (crowdsourced))
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
ClassificationonRAFT
NIS· 2022-05-11
0.833
best: 0.857 (Human (crowdsourced))
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
ClassificationonRAFT
SRI· 2022-05-11
0.508
best: 0.516 (GPT-3)
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
ClassificationonRAFT
TC· 2022-05-11
0.879
best: 0.897 (Human (crowdsourced))
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638
ClassificationonRAFT
TEH· 2022-05-11
0.586
best: 0.722 (Human (crowdsourced))
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning arXiv:2205.05638