Abstract
State-of-the-art language models in NLP perform best when fine-tuned even on small datasets, but due to their increasing size, fine-tuning and downstream usage have become extremely compute-intensive. Being able to efficiently and effectively fine-tune the largest pre-trained models is thus key in order to reap the benefits of the latest advances in NLP. In this tutorial, we provide a comprehensive overview of parameter-efficient fine-tuning methods. We highlight their similarities and differences by presenting them in a unified view. We explore the benefits and usage scenarios of a neglected property of such parameter-efficient models—modularity—such as composition of modules to deal with previously unseen data conditions. We finally highlight how both properties——parameter efficiency and modularity——can be useful in the real-world setting of adapting pre-trained models to under-represented languages and domains with scarce annotated data for several downstream applications.- Anthology ID:
- 2022.emnlp-tutorials.5
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dubai, UAE
- Editors:
- Samhaa R. El-Beltagy, Xipeng Qiu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 23–29
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-tutorials.5
- DOI:
- 10.18653/v1/2022.emnlp-tutorials.5
- Cite (ACL):
- Sebastian Ruder, Jonas Pfeiffer, and Ivan Vulić. 2022. Modular and Parameter-Efficient Fine-Tuning for NLP Models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts, pages 23–29, Abu Dubai, UAE. Association for Computational Linguistics.
- Cite (Informal):
- Modular and Parameter-Efficient Fine-Tuning for NLP Models (Ruder et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2022.emnlp-tutorials.5.pdf