A wide range of NLP tasks benefit from the fine-tuning of pretrained language models (PLMs). However, a number of redundant parameters which contribute less to the downstream task are observed in a directly fine-tuned model. We consider the gap between pretraining and downstream tasks hinders the training of these redundant parameters, and results in a suboptimal performance of the overall model. In this paper, we present PATS (Perturbation According To Sensitivity), a noisy training mechanism which considers each parameter's importance in the downstream task to help fine-tune PLMs. The main idea of PATS is to add bigger noise to parameters with lower sensitivity and vice versa, in order to activate more parameters' contributions to downstream tasks without affecting the sensitive ones much. Extensive experiments conducted on different tasks of the GLUE benchmark show PATS can consistently empower the fine-tuning of different sizes of PLMs, and the parameters in the well-performing models always have more concentrated distributions of sensitivities, which experimentally proves the effectiveness of our method.

PATS: Sensitivity-aware Noisy Learning for Pretrained Language Models

**Topics:** Machine Learning for NLP 
Machine Translation 
Multilinguality 
NLP Applications 
Natural Language Generation 
Phonology, Morphology and Word Segmentation 
Question Answering 
Resources and Evaluation 
Semantics: Lexical, Sentence level, Textual Inference and Other areas 
Summarization 
Syntax, Parsing and their Applications 
Theme Track 
Unsupervised and Weakly-Supervised Methods in NLP

Poster Session 10

poster

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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

Yupeng Zhang

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PATS: Sensitivity-aware Noisy Learning for Pretrained Language Models

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