Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representations

• URL: http://arxiv.org/abs/2211.08794v4
• Date: Fri, 26 May 2023 16:47:18 GMT
• Title: Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representations
• Authors: Linlin Liu, Xingxuan Li, Megh Thakkar, Xin Li, Shafiq Joty, Luo Si, Lidong Bing
• Abstract summary: Fine-tuning of pretrained language models (PLMs) is prone to overfitting in the low resource scenarios. We present a novel method that operates on the hidden representations of a PLM to reduce overfitting.
• Score: 51.75960511842552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the huge amount of parameters, fine-tuning of pretrained language models (PLMs) is prone to overfitting in the low resource scenarios. In this work, we present a novel method that operates on the hidden representations of a PLM to reduce overfitting. During fine-tuning, our method inserts random autoencoders between the hidden layers of a PLM, which transform activations from the previous layers into multi-view compressed representations before feeding them into the upper layers. The autoencoders are plugged out after fine-tuning, so our method does not add extra parameters or increase computation cost during inference. Our method demonstrates promising performance improvement across a wide range of sequence- and token-level low-resource NLP tasks.

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