@inproceedings{kobayashi-etal-2023-transformer,
    title = "Transformer Language Models Handle Word Frequency in Prediction Head",
    author = "Kobayashi, Goro  and
      Kuribayashi, Tatsuki  and
      Yokoi, Sho  and
      Inui, Kentaro",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.276",
    doi = "10.18653/v1/2023.findings-acl.276",
    pages = "4523--4535",
    abstract = "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.",
}

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.
Kobayashi, Goro Kuribayashi, Tatsuki Yokoi, Sho Inui, Kentaro
Transformer Language Models Handle Word Frequency in Prediction Head