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    <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.</abstract>
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%0 Conference Proceedings
%T Modular and Parameter-Efficient Fine-Tuning for NLP Models
%A Ruder, Sebastian
%A Pfeiffer, Jonas
%A Vulić, Ivan
%Y El-Beltagy, Samhaa R.
%Y Qiu, Xipeng
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dubai, UAE
%F ruder-etal-2022-modular
%X 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.
%R 10.18653/v1/2022.emnlp-tutorials.5
%U https://aclanthology.org/2022.emnlp-tutorials.5
%U https://doi.org/10.18653/v1/2022.emnlp-tutorials.5
%P 23-29

Markdown (Informal)

[Modular and Parameter-Efficient Fine-Tuning for NLP Models](https://aclanthology.org/2022.emnlp-tutorials.5) (Ruder et al., EMNLP 2022)

• Modular and Parameter-Efficient Fine-Tuning for NLP Models (Ruder et al., EMNLP 2022)

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.