Regularized Fine-tuning Strategies for Neural Language Models : Application of entropy regularization on GPT-2

Abstract: Deep neural language models like GPT-2 is undoubtedly strong at text generation, but often requires special decoding strategies to prevent producing degenerate output - namely repetition. The use of maximum likelihood training objective results in a peaked probability distribution, leading to the over-confidence of neural networks. In this thesis, we explore entropy regularization for a neural language model that can easily smooth peaked output distribution during the fine-tuning process employing GPT-2. We first define the models in three ways: (1) Out of-the box model without fine-tuning process, (2) Fine-tuned model without entropy regularization, and (3) Fine-tuned model with entropy regularization. To investigate the effect of domains on the model, we also divide the dataset into three ways: (1) fine-tuned on heterogeneous dataset, tested on heterogeneous dataset, (2) fine-tuned on homogeneous dataset, tested on homogeneous dataset, and (3) fine-tuned on heterogeneous dataset, tested on homogeneous dataset. In terms of entropy regularization, we experiment controlling the entropy strength parameter (