Byung-Doh Oh

Byung-Doh Oh has 3 papers in our library, in 2023–2026, most of them on Language models & pretraining, Psycholinguistics & language acquisition; often with William Schuler.

3 papers in our library · 2023–2026

current affiliationNanyang Technological University · Singapore Singapore

The profile is the library's match of the name; two people with one name are not always told apart.

Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

The most cited first (OpenAlex and our library).

Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times? (2023 · 211 citations)
Classic psycholinguistic studies found that language models with lower perplexity produce surprisal values that better predict human reading behavior.
The Impact of Token Granularity on the Predictive Power of Language Model Surprisal (2025 · 12 citations)
Cognitive modeling often uses word-level surprisal from language models to predict how hard words are for readers, but the effect of subword token granularity has rarely been examined.
Clozing the Gap: Exploring Why Language Model Surprisal Outperforms Cloze Surprisal (2026 · 1 citation)
The paper asks why next-word probabilities from language models predict reading times better than probabilities estimated from human cloze responses. This matters because the choice of predictability measure can change scientific conclusions about prediction in comprehension.

1 co-author with a page, the most papers together first.

Papers of our library cite their works 117 times (of the 140,884 papers whose bibliographies we read); 13 citing papers with a page here →.