Better Fine-Tuning by Reducing Representational Collapse

6 Aug 2020Armen AghajanyanAkshat ShrivastavaAnchit GuptaNaman GoyalLuke ZettlemoyerSonal Gupta

Although widely adopted, existing approaches for fine-tuning pre-trained language models have been shown to be unstable across hyper-parameter settings, motivating recent work on trust region methods. In this paper, we present a simplified and efficient method rooted in trust region theory that replaces previously used adversarial objectives with parametric noise (sampling from either a normal or uniform distribution), thereby discouraging representation change during fine-tuning when possible without hurting performance... (read more)

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