Byung-Doh Oh

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

Byung-Doh Oh, William Schuler · Transactions of the Association for Computational Linguistics · 2023 · 77 citations · Full text

Llm Fine-tuning, Engineering, Multilingualism +25

Comparison of Structural Parsers and Neural Language Models as Surprisal Estimators

Byung-Doh Oh, Christian Clark, William Schuler · Frontiers in Artificial Intelligence · 2022 · 39 citations · Full text

Surprisal Estimators for Human Reading Times Need Character Models

Byung-Doh Oh, Christian Clark, William Schuler · 2021 · 16 citations · Full text

Engineering, Part-of-speech Tagging, Surprisal Estimators +20

Modeling morphological learning, typology, and change: What can the neural sequence-to-sequence framework contribute?

Micha Elsner, Andrea D. Sims, Alexander Erdmann et al. · Journal of Language Modelling · 2019 · 13 citations · Full text

Language Evolution, Neurolinguistics, Psycholinguistics +19

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

Byung-Doh Oh, William Schuler · 2022 · 11 citations · Full text

Llm Fine-tuning, Above Gpt-2 Surprisal, Neurolinguistics +22