Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning
Zhaojiang Lin, Andrea Madotto, Pascale Fung · 2020
Fine-tuning pre-trained generative language models to down-stream language generation tasks has shown promising results.However, this comes with the cost of having a single, large model for each task, which is not ideal in low-memory/power scenarios (e.g., mobile).In this paper, we propose an effective way to fine-tune multiple down-stream generation tasks simultaneously using a single, large pretrained model.The experiments on five diverse language generation tasks show that by just using an additional 2-3% parameters for each task, our model can maintain or even improve the performance of fine-tuning the whole model 1 .