Abstract
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.- Anthology ID:
- 2020.findings-emnlp.41
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 441–459
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.41
- DOI:
- 10.18653/v1/2020.findings-emnlp.41
- Cite (ACL):
- Zhaojiang Lin, Andrea Madotto, and Pascale Fung. 2020. Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 441–459, Online. Association for Computational Linguistics.
- Cite (Informal):
- Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning (Lin et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2020.findings-emnlp.41.pdf
- Code
- zlinao/VGLM
- Data
- CoQA