Team Ohio State at CMCL 2021 Shared Task: Fine-Tuned RoBERTa for Eye-Tracking Data Prediction. - Byung-Doh Oh (CMLS, 2021)

Contributions of Propositional Content and Syntactic Category Information in Sentence Processing. - Byung-Doh Oh; William Schuler (CMLS, 2021)

Surprisal Estimators for Human Reading Times Need Character Models. - Byung-Doh Oh; Christian Clark; William Schuler (ACL/IJCNLP (1), 2021)

Coreference-aware Surprisal Predicts Brain Response. - Evan Jaffe; Byung-Doh Oh; William Schuler (EMNLP (Findings), 2021)

Character-based PCFG Induction for Modeling the Syntactic Acquisition of Morphologically Rich Languages. - Lifeng Jin; Byung-Doh Oh; William Schuler (EMNLP (Findings), 2021)

Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 Surprisal. - Byung-Doh Oh; William Schuler (EMNLP, 2022)

Comparison of Structural Parsers and Neural Language Models as Surprisal Estimators. - Byung-Doh Oh; Christian Clark; William Schuler (Frontiers Artif. Intell., 2022)

Token-wise Decomposition of Autoregressive Language Model Hidden States for Analyzing Model Predictions. - Byung-Doh Oh; William Schuler (ACL (1), 2023)

Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens. - Byung-Doh Oh; William Schuler (EMNLP (Findings), 2023)

Transformer-Based LM Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens. - Byung-Doh Oh; William Schuler (CoRR, 2023)

Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times? - Byung-Doh Oh; William Schuler (Trans. Assoc. Comput. Linguistics, 2023)

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 (EACL (1), 2024)

Leading Whitespaces of Language Models' Subword Vocabulary Pose a Confound for Calculating Word Probabilities. - Byung-Doh Oh; William Schuler (EMNLP, 2024)

Leading Whitespaces of Language Models' Subword Vocabulary Poses a Confound for Calculating Word Probabilities. - Byung-Doh Oh; William Schuler (CoRR, 2024)

The Impact of Token Granularity on the Predictive Power of Language Model Surprisal. - Byung-Doh Oh; William Schuler (ACL (1), 2025)

The Inverse Scaling Effect of Pre-Trained Language Model Surprisal Is Not Due to Data Leakage. - Byung-Doh Oh; Hongao Zhu; William Schuler (ACL (Findings), 2025)

Linear Recency Bias During Training Improves Transformers' Fit to Reading Times. - Christian Clark; Byung-Doh Oh; William Schuler (COLING, 2025)

How Well Does First-Token Entropy Approximate Word Entropy as a Psycholinguistic Predictor? - Christian Clark; Byung-Doh Oh; William Schuler (CoRR, 2025)