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2020 – today
- 2024
- [c9]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: 2644-2663 - [i6]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 abs/2402.02255 (2024) - [i5]Byung-Doh Oh, William Schuler:
Leading Whitespaces of Language Models' Subword Vocabulary Poses a Confound for Calculating Word Probabilities. CoRR abs/2406.10851 (2024) - 2023
- [j2]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: 336-350 (2023) - [c8]Byung-Doh Oh, William Schuler:
Token-wise Decomposition of Autoregressive Language Model Hidden States for Analyzing Model Predictions. ACL (1) 2023: 10105-10117 - [c7]Byung-Doh Oh, William Schuler:
Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens. EMNLP (Findings) 2023: 1915-1921 - [i4]Byung-Doh Oh, William Schuler:
Transformer-Based LM Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens. CoRR abs/2304.11389 (2023) - [i3]Byung-Doh Oh, William Schuler:
Token-wise Decomposition of Autoregressive Language Model Hidden States for Analyzing Model Predictions. CoRR abs/2305.10614 (2023) - 2022
- [j1]Byung-Doh Oh, Christian Clark, William Schuler:
Comparison of Structural Parsers and Neural Language Models as Surprisal Estimators. Frontiers Artif. Intell. 5: 777963 (2022) - [c6]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: 9324-9334 - [i2]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 abs/2212.11185 (2022) - [i1]Byung-Doh Oh, William Schuler:
Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times? CoRR abs/2212.12131 (2022) - 2021
- [c5]Byung-Doh Oh, Christian Clark, William Schuler:
Surprisal Estimators for Human Reading Times Need Character Models. ACL/IJCNLP (1) 2021: 3746-3757 - [c4]Byung-Doh Oh:
Team Ohio State at CMCL 2021 Shared Task: Fine-Tuned RoBERTa for Eye-Tracking Data Prediction. CMLS 2021: 97-101 - [c3]Byung-Doh Oh, William Schuler:
Contributions of Propositional Content and Syntactic Category Information in Sentence Processing. CMLS 2021: 241-250 - [c2]Evan Jaffe, Byung-Doh Oh, William Schuler:
Coreference-aware Surprisal Predicts Brain Response. EMNLP (Findings) 2021: 3351-3356 - [c1]Lifeng Jin, Byung-Doh Oh, William Schuler:
Character-based PCFG Induction for Modeling the Syntactic Acquisition of Morphologically Rich Languages. EMNLP (Findings) 2021: 4367-4378
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