Byung-Doh Oh

According to our database, Byung-Doh Oh authored at least 12 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

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, William Schuler

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

2023

Transformer-Based LM Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens.

Byung-Doh Oh, William Schuler

CoRR, 2023

Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens.

Byung-Doh Oh, William Schuler

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

Token-wise Decomposition of Autoregressive Language Model Hidden States for Analyzing Model Predictions.

Byung-Doh Oh, William Schuler

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

2022

Comparison of Structural Parsers and Neural Language Models as Surprisal Estimators.

Byung-Doh Oh, Christian Clark, William Schuler

Frontiers Artif. Intell., 2022

Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?

Byung-Doh Oh, William Schuler

CoRR, 2022

Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 Surprisal.

Byung-Doh Oh, William Schuler

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

2021

Character-based PCFG Induction for Modeling the Syntactic Acquisition of Morphologically Rich Languages.

Lifeng Jin, Byung-Doh Oh, William Schuler

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021, 2021

Coreference-aware Surprisal Predicts Brain Response.

Evan Jaffe, Byung-Doh Oh, William Schuler

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021, 2021

Surprisal Estimators for Human Reading Times Need Character Models.

Byung-Doh Oh, Christian Clark, William Schuler

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021

Contributions of Propositional Content and Syntactic Category Information in Sentence Processing.

Byung-Doh Oh, William Schuler

Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, 2021

Team Ohio State at CMCL 2021 Shared Task: Fine-Tuned RoBERTa for Eye-Tracking Data Prediction.

Byung-Doh Oh

Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, 2021