Byung-Doh Oh (오병도)

I am a Faculty Fellow at the Center for Data Science at New York University, where I collaborate with Tal Linzen. I received my PhD in computational linguistics from The Ohio State University, where I worked with William Schuler.

My work aims to advance our understanding of language processing in humans and machines by drawing on techniques from psycholinguistics and machine learning. I am particularly interested in developing computational models that capture the real-time processing behavior of human language users, and interpretability techniques for studying the predictions and representations of neural networks.

representative publications

JML

Dissociable frequency effects attenuate as large language model surprisal predictors improve

Byung-Doh Oh, and William Schuler

Journal of Memory and Language, 2025

Bib PDF

@article{ohschuler25jml,
  author = {Oh, Byung-Doh and Schuler, William},
  title = {Dissociable frequency effects attenuate as large language model surprisal predictors improve},
  year = {2025},
  journal = {Journal of Memory and Language},
  volume = {143},
  pages = {104645},
  section = {articles},
}

EACL

Frequency explains the inverse correlation of large language models’ size, training data amount, and surprisal’s fit to reading times

Byung-Doh Oh, Shisen Yue, and William Schuler

In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics, 2024

Bib PDF Slides

@inproceedings{ohetal24eacl,
  author = {Oh, Byung-Doh and Yue, Shisen and Schuler, William},
  title = {Frequency explains the inverse correlation of large language models' size, training data amount, and surprisal's fit to reading times},
  year = {2024},
  booktitle = {Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics},
  pages = {2644--2663},
  section = {papers},
}

ACL

Token-wise decomposition of autoregressive language model hidden states for analyzing model predictions

Byung-Doh Oh, and William Schuler

In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, 2023

Bib PDF Poster

@inproceedings{ohschuler23acl,
  author = {Oh, Byung-Doh and Schuler, William},
  title = {Token-wise decomposition of autoregressive language model hidden states for analyzing model predictions},
  year = {2023},
  booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics},
  pages = {10105--10117},
  section = {papers},
}

TACL

Why does surprisal from larger Transformer-based language models provide a poorer fit to human reading times?

Byung-Doh Oh, and William Schuler

Transactions of the Association for Computational Linguistics, 2023

Bib PDF Slides

@article{ohschuler23tacl,
  author = {Oh, Byung-Doh and Schuler, William},
  title = {Why does surprisal from larger Transformer-based language models provide a poorer fit to human reading times?},
  year = {2023},
  journal = {Transactions of the Association for Computational Linguistics},
  volume = {11},
  pages = {336--350},
  section = {articles},
}

EMNLP

Entropy- and distance-based predictors from GPT-2 attention patterns predict reading times over and above GPT-2 surprisal

Byung-Doh Oh, and William Schuler

In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

Bib PDF Poster

@inproceedings{ohschuler22emnlp,
  author = {Oh, Byung-Doh and Schuler, William},
  title = {Entropy- and distance-based predictors from GPT-2 attention patterns predict reading times over and above GPT-2 surprisal},
  year = {2022},
  booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
  pages = {9324--9334},
  section = {papers},
}

FAI

Comparison of structural parsers and neural language models as surprisal estimators

Byung-Doh Oh, Christian Clark, and William Schuler

Frontiers in Artificial Intelligence, 2022

Bib PDF

@article{ohetal22fai,
  author = {Oh, Byung-Doh and Clark, Christian and Schuler, William},
  title = {Comparison of structural parsers and neural language models as surprisal estimators},
  year = {2022},
  journal = {Frontiers in Artificial Intelligence},
  volume = {5},
  pages = {777963},
  section = {articles},
}