---
title: "Who cites Byung-Doh Oh"
description: "111 papers of the Elamur Research library cite the works of Byung-Doh Oh, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/byung-doh-oh/cited-by/"
---
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# Who cites Byung-Doh Oh

117 citations of their works from 111 papers of our library (of the 140,884 whose bibliographies we read) · 13 with a page here, the most cited first · [← Byung-Doh Oh](https://md.research.elamur.ai/free/en/authors/byung-doh-oh/)

1. [**Testing the Predictions of Surprisal Theory in 11 Languages**Ethan G. Wilcox, Tiago Pimentel, Clara Meister, et al.*2023 · 158 citations*](https://md.research.elamur.ai/free/en/papers/wilcox-2023-testing-predictions-surprisal-theory-11/)
2. [**On the Proper Treatment of Tokenization in Psycholinguistics**Mario Giulianelli, Luca Malagutti, Juan Luis Gastaldi, et al.*2024 · 23 citations*](https://md.research.elamur.ai/free/en/papers/giulianelli-2024-proper-treatment-tokenization-psycholinguistics/)
3. [**The Impact of Token Granularity on the Predictive Power of Language Model Surprisal**Byung-Doh Oh, William Schuler*2025 · 12 citations*](https://md.research.elamur.ai/free/en/papers/oh-2025-impact-token-granularity-predictive-power/)
4. [**Are Frequent Phrases Directly Retrieved like Idioms? An Investigation with Self-Paced Reading and Language Models**Giulia Rambelli, Emmanuele Chersoni, Marco S. G. Senaldi, et al.*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/rambelli-2023-are-frequent-phrases-directly-retrieved/)
5. [**An Existence Proof for Neural Language Models That Can Explain Garden-Path Effects via Surprisal**Ryo Yoshida, Shinnosuke Isono, Taiga Someya, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/yoshida-2026-existence-proof-neural-language-models/)
6. [**Clozing the Gap: Exploring Why Language Model Surprisal Outperforms Cloze Surprisal**Sathvik Nair, Byung-Doh Oh*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/nair-2026-clozing-gap-exploring-why-language/)
7. [**Human-like Fleeting Memory Improves Language Learning but Impairs Reading Time Prediction in Transformer Language Models**Abishek Thamma, Micha Heilbron*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/thamma-2026-human-like-fleeting-memory-improves/)
8. [**Probing for Reading Times**Eleftheria Tsipidi, Samuel Kiegeland, Francesco Ignazio Re, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/tsipidi-2026-probing-reading-times/)
9. [**Surprisal and Metaphor Novelty Judgments: Moderate Correlations and Divergent Scaling Effects Revealed by Corpus-Based and Synthetic Datasets**Omar Momen, Emilie Sitter, Berenike Herrmann, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/momen-2026-surprisal-metaphor-novelty-judgments-moderate/)
10. [**A Transparent Model of Syntactic and Semantic Cue-based Retrieval**Shisen Yue, John T. Hale*2026*](https://md.research.elamur.ai/free/en/papers/yue-2026-transparent-model-syntactic-semantic-cue/)
11. [**Causal Inferences Are Driven by Noun Concept Specificity Evidence from Self-Paced Reading and Large Language Model Surprisal**Fabian Schlotterbeck, Raphael Barth*2026*](https://md.research.elamur.ai/free/en/papers/schlotterbeck-2026-causal-inferences-are-driven-noun/)
12. [**Open-access Dataset on Acceptability Ratings of Korean Clausal Constructions by Humans and GPT Models**Gyu-Ho Shin, Soo-Hwan Lee, Chanyoung Lee*2026*](https://md.research.elamur.ai/free/en/papers/shin-2026-open-access-dataset-acceptability-ratings/)
13. [**Word predictability estimates from language models are not robust to tokenizer vocabulary**Kien Nguyen, Suhas Arehalli*2026*](https://md.research.elamur.ai/free/en/papers/nguyen-2026-word-predictability-estimates-language-models/)

98 more citing papers of our library have no page here yet.

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