Gradient Ascent Post-training Enhances Language Model Generalization

Dongkeun Yoon; Joel Jang; Sungdong Kim; Minjoon Seo

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arXiv · 2306.07052

Gradient Ascent Post-training Enhances Language Model Generalization

Dongkeun Yoon·Joel Jang·Sungdong Kim·Minjoon Seo

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Abstract

In this work, we empirically show that updating pretrained LMs (350M, 1.3B, 2.7B) with just a few steps of Gradient Ascent Post-training (GAP) on random, unlabeled text corpora enhances its zero-shot generalization capabilities across diverse NLP tasks. Specifically, we show that GAP can allow LMs to become comparable to 2-3x times larger LMs across 12 different NLP tasks. We also show that applying GAP on out-of-distribution corpora leads to the most reliable performance improvements. Our findings indicate that GAP can be a promising method for improving the generalization capability of LMs without any task-specific fine-tuning.

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Dongkeun Yoon, Joel Jang, Sungdong Kim, Minjoon Seo. 2023-06-12. Gradient Ascent Post-training Enhances Language Model Generalization. https://arxiv.org/abs/2306.07052

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