A Short Study on Compressing Decoder-Based Language Models

Tianda Li; Yassir El Mesbahi; Ivan Kobyzev; Ahmad Rashid; Atif Mahmud; Nithin Anchuri; Habib Hajimolahoseini; Yang Liu; Mehdi Rezagholizadeh

arXiv · 2110.08460

A Short Study on Compressing Decoder-Based Language Models

Tianda Li·Yassir El Mesbahi·Ivan Kobyzev·Ahmad Rashid·Atif Mahmud·Nithin Anchuri·Habib Hajimolahoseini·Yang Liu·Mehdi Rezagholizadeh

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Abstract

Pre-trained Language Models (PLMs) have been successful for a wide range of natural language processing (NLP) tasks. The state-of-the-art of PLMs, however, are extremely large to be used on edge devices. As a result, the topic of model compression has attracted increasing attention in the NLP community. Most of the existing works focus on compressing encoder-based models (tiny-BERT, distilBERT, distilRoBERTa, etc), however, to the best of our knowledge, the compression of decoder-based models (such as GPT-2) has not been investigated much. Our paper aims to fill this gap. Specifically, we explore two directions: 1) we employ current state-of-the-art knowledge distillation techniques to improve fine-tuning of DistilGPT-2. 2) we pre-train a compressed GPT-2 model using layer truncation and compare it against the distillation-based method (DistilGPT2). The training time of our compressed model is significantly less than DistilGPT-2, but it can achieve better performance when fine-tuned on downstream tasks. We also demonstrate the impact of data cleaning on model performance.

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Tianda Li, Yassir El Mesbahi, Ivan Kobyzev, Ahmad Rashid, Atif Mahmud, Nithin Anchuri, Habib Hajimolahoseini, Yang Liu, Mehdi Rezagholizadeh. 2021-10-16. A Short Study on Compressing Decoder-Based Language Models. https://arxiv.org/abs/2110.08460

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