Byung-Doh Oh

Publication Activity (10 Years)

Years Active: 2021-2024
Publications (10 Years): 17

Top Topics

Term Dependencies

Top Venues

EMNLP (Findings)

Publications

Byung-Doh Oh, Shisen Yue, William Schuler
Frequency Explains the Inverse Correlation of Large Language Models' Size, Training Data Amount, and Surprisal's Fit to Reading Times. EACL (1) (2024)
Byung-Doh Oh, Shisen Yue, William Schuler
Frequency Explains the Inverse Correlation of Large Language Models' Size, Training Data Amount, and Surprisal's Fit to Reading Times. CoRR (2024)
Byung-Doh Oh, William Schuler
Leading Whitespaces of Language Models' Subword Vocabulary Poses a Confound for Calculating Word Probabilities. CoRR (2024)
Byung-Doh Oh, William Schuler
Token-wise Decomposition of Autoregressive Language Model Hidden States for Analyzing Model Predictions. ACL (1) (2023)
Byung-Doh Oh, William Schuler
Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens. EMNLP (Findings) (2023)
Byung-Doh Oh, William Schuler
Transformer-Based LM Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens. CoRR (2023)
Byung-Doh Oh, William Schuler
Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times? Trans. Assoc. Comput. Linguistics 11 (2023)
Byung-Doh Oh, William Schuler
Token-wise Decomposition of Autoregressive Language Model Hidden States for Analyzing Model Predictions. CoRR (2023)
Byung-Doh Oh, William Schuler
Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times? CoRR (2022)
Byung-Doh Oh, Christian Clark, William Schuler
Comparison of Structural Parsers and Neural Language Models as Surprisal Estimators. Frontiers Artif. Intell. 5 (2022)
Byung-Doh Oh, William Schuler
Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 Surprisal. CoRR (2022)
Byung-Doh Oh, William Schuler
Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 Surprisal. EMNLP (2022)
Byung-Doh Oh, Christian Clark, William Schuler
Surprisal Estimators for Human Reading Times Need Character Models. ACL/IJCNLP (1) (2021)
Byung-Doh Oh, William Schuler
Contributions of Propositional Content and Syntactic Category Information in Sentence Processing. CMLS (2021)
Byung-Doh Oh
Team Ohio State at CMCL 2021 Shared Task: Fine-Tuned RoBERTa for Eye-Tracking Data Prediction. CMLS (2021)
Lifeng Jin, Byung-Doh Oh, William Schuler
Character-based PCFG Induction for Modeling the Syntactic Acquisition of Morphologically Rich Languages. EMNLP (Findings) (2021)
Evan Jaffe, Byung-Doh Oh, William Schuler
Coreference-aware Surprisal Predicts Brain Response. EMNLP (Findings) (2021)