Ahead-of-Time P-Tuning
In this paper, we propose Ahead-of-Time (AoT) P-Tuning, a novel parameter-efficient fine-tuning method for pre-trained Language Models (LMs) that adds input-dependent bias before each Transformer layer. We evaluate AoT P-Tuning on GLUE and SuperGLUE benchmarking datasets using RoBERTa and DeBERTa models, showing that it outperforms BitFit and is comparable or better than other baseline methods for efficient fine-tuning. Additionally, we assess the inference overhead of AoT P-Tuning and demonstrate that it introduces negligible overhead compared to established baseline methods. Our method enables multi-task inference with a single backbone LM, making it a practical solution for real-world applications.
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GLUE
SuperGLUE
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Methods
Absolute Position Encodings •
Adam •
Attention Dropout •
BERT •
BPE •
DeBERTa •
Dense Connections •
Disentangled Attention Mechanism •
Dropout •
GELU •
Label Smoothing •
Layer Normalization •
Linear Layer •
Linear Warmup With Linear Decay •
Multi-Head Attention •
Position-Wise Feed-Forward Layer •
Residual Connection •
RoBERTa •
Scaled Dot-Product Attention •
Softmax •
Transformer •
Weight Decay •
WordPiece
