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Improving Domain Adaptation of Transformer Models For Generating Reddit Comments

We improve upon the recent success of large language models based on the transformer architecture by investigating and showing several methods that have empirically improved its performance in domain adaptation. We use a pre-trained GPT-2 model and perform fine-tuning on 5 different subreddits, and use different methods of ordering the training data based on our priors about the input to see how this affects the prediction quality of the trained model. We propose a new metric for evaluating causal language modeling tasks called APES (Average Perplexity Evaluation for Sentences) to address the limitations of existing metrics, and apply them to our results. Our results are evaluated against both LSTM and GPT-2 baselines.