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arXiv · 2305.10835

Ahead-of-Time P-Tuning

Daniil Gavrilov·Nikita Balagansky
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Abstract

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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Daniil Gavrilov, Nikita Balagansky. 2023-05-18. Ahead-of-Time P-Tuning. https://arxiv.org/abs/2305.10835

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