  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.
