Transformer Language Models Handle Word Frequency in Prediction Head
Goro Kobayashi Tatsuki Kuribayashi Sho Yokoi Kentaro Inui
Prediction head is a crucial component of Transformer language models.
Despite its direct impact on prediction, this component has often been
overlooked in analyzing Transformers. In this study, we investigate the inner
workings of the prediction head, specifically focusing on bias parameters. Our
experiments with BERT and GPT-2 models reveal that the biases in their word
prediction heads play a significant role in the models' ability to reflect word
frequency in a corpus, aligning with the logit adjustment method commonly used
in long-tailed learning. We also quantify the effect of controlling the biases
in practical auto-regressive text generation scenarios; under a particular
setting, more diverse text can be generated without compromising text quality.