| 2026 | ACL | Clozing the Gap: Exploring Why Language Model Surprisal Outperforms Cloze Surprisal. | Sathvik Nair, Byung-Doh Oh |
| 2025 | ACL | The Impact of Token Granularity on the Predictive Power of Language Model Surprisal. | Byung-Doh Oh, William Schuler |
| 2025 | ACL | The Inverse Scaling Effect of Pre-Trained Language Model Surprisal Is Not Due to Data Leakage. | Byung-Doh Oh, Hongao Zhu, William Schuler |
| 2025 | COLING | Linear Recency Bias During Training Improves Transformers' Fit to Reading Times. | Christian Clark, Byung-Doh Oh, William Schuler |
| 2025 | IJCNLP | How Well Does First-Token Entropy Approximate Word Entropy as a Psycholinguistic Predictor? | Christian Clark, Byung-Doh Oh, William Schuler |
| 2024 | 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, William Schuler |
| 2024 | EMNLP | Leading Whitespaces of Language Models' Subword Vocabulary Pose a Confound for Calculating Word Probabilities. | Byung-Doh Oh, William Schuler |
| 2023 | ACL | Token-wise Decomposition of Autoregressive Language Model Hidden States for Analyzing Model Predictions. | Byung-Doh Oh, William Schuler |
| 2023 | EMNLP | Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens. | Byung-Doh Oh, William Schuler |
| 2022 | EMNLP | Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 Surprisal. | Byung-Doh Oh, William Schuler |
| 2021 | ACL | Surprisal Estimators for Human Reading Times Need Character Models. | Byung-Doh Oh, Christian Clark, William Schuler |
| 2021 | EMNLP | Coreference-aware Surprisal Predicts Brain Response. | Evan Jaffe, Byung-Doh Oh, William Schuler |
| 2021 | EMNLP | Character-based PCFG Induction for Modeling the Syntactic Acquisition of Morphologically Rich Languages. | Lifeng Jin, Byung-Doh Oh, William Schuler |