Modular and Parameter-Efficient Fine-Tuning for NLP Models

Summary: 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.

Speaker: Jonas Pfeiffer is a Research Scientist at Google Research. He is interested in modular representation learning in multi-task, multilingual, and multi-modal contexts, and in low-resource scenarios. He worked on his PhD at the Technical University of Darmstadt, was a visiting researcher at the New York University and a Research Scientist Intern at Meta Research. Jonas has received the IBM PhD Research Fellowship award for 2021/2022. He has given numerous invited talks at academia, industry and ML summer schools, and has co-organized multiple workshops on multilinguality and multimodality.

Also available online: https://uzh.zoom.us/j/62400025916?pwd=SElUS2QzOWVuRi9KdVREK2xIQUk3dz09

Additional Information: Guest lecture for the seminar course “Multimodal Multilingual Natural Language Processing”. We will have a small Apero after the talk in the CL coffee lounge. There will be drinks and some snacks.