Byung-Doh Oh
16 papers · 2019–2026 · 6 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
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🗺️ Taxonomy Completionist (31) 🏃 Academic Marathon · Sprint 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Silver 🐣 Hot Topic Early Bird
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Keyword Champion
(2)
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Dynamic Duo
(13)
🧬
Topic Evolution
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Prolific Year · Productive
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Unstoppable · Bronze
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The Questioner
💎
Century Club · Bronze
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Keyword Collector
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Top co-authors
Research topics
Artificial Intelligence
(11)
Machine Learning
(11)
Core AI
(11)
Natural Language Processing
(9)
Core Methods
(7)
Deep Learning
(6)
Generation
(5)
Interdisciplinary
(4)
Cognitive Science
(4)
Optimization & Theory
(4)
Learning Types
(3)
Models
(3)
Resources & Methods
(3)
Understanding
(2)
Mathematics & Optimization
(1)
Keywords
reading time
(7)
language model
(5)
psycholinguistic modeling
(3)
language modeling
(2)
cognitive modeling
(2)
word frequency
(2)
language model surprisal
(2)
reading time prediction
(2)
self-paced reading
(2)
grammar induction
(1)
sentence processing
(1)
semantic processing
(1)
probabilistic modeling
(1)
hidden state
(1)
incremental parsing
(1)
brain imaging
(1)
attention mechanism
(1)
pre-trained language model
(1)
token importance
(1)
probabilistic context-free grammar
(1)
Papers
Clozing the Gap: Exploring Why Language Model Surprisal Outperforms Cloze Surprisal
ACL 2026
How Well Does First-Token Entropy Approximate Word Entropy as a Psycholinguistic Predictor?
IJCNLP 2025
Linear Recency Bias During Training Improves Transformers’ Fit to Reading Times
COLING 2025
The Impact of Token Granularity on the Predictive Power of Language Model Surprisal
ACL 2025
The Inverse Scaling Effect of Pre-Trained Language Model Surprisal Is Not Due to Data Leakage
ACL 2025
Frequency Explains the Inverse Correlation of Large Language Models’ Size, Training Data Amount, and Surprisal’s Fit to Reading Times
EACL 2024
Leading Whitespaces of Language Models’ Subword Vocabulary Pose a Confound for Calculating Word Probabilities
EMNLP 2024
Token-wise Decomposition of Autoregressive Language Model Hidden States for Analyzing Model Predictions
ACL 2023
Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens
EMNLP 2023
Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 Surprisal
EMNLP 2022
Character-based PCFG Induction for Modeling the Syntactic Acquisition of Morphologically Rich Languages
EMNLP 2021
Contributions of Propositional Content and Syntactic Category Information in Sentence Processing
NAACL 2021
Coreference-aware Surprisal Predicts Brain Response
EMNLP 2021
Surprisal Estimators for Human Reading Times Need Character Models
ACL 2021
Team Ohio State at CMCL 2021 Shared Task: Fine-Tuned RoBERTa for Eye-Tracking Data Prediction
NAACL 2021
THOMAS: The Hegemonic OSU Morphological Analyzer using Seq2seq
ACL 2019