Computer Science > Computation and Language
arXiv:2603.09872 (cs)
[Submitted on 10 Mar 2026]
Title:N-gram-like Language Models Predict Reading Time Best
View a PDF of the paper titled N-gram-like Language Models Predict Reading Time Best, by James A. Michaelov and Roger P. Levy
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Abstract:Recent work has found that contemporary language models such as transformers can become so good at next-word prediction that the probabilities they calculate become worse for predicting reading time. In this paper, we propose that this can be explained by reading time being sensitive to simple n-gram statistics rather than the more complex statistics learned by state-of-the-art transformer language models. We demonstrate that the neural language models whose predictions are most correlated with n-gram probability are also those that calculate probabilities that are the most correlated with eye-tracking-based metrics of reading time on naturalistic text.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2603.09872 [cs.CL] |
| (or arXiv:2603.09872v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09872
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View a PDF of the paper titled N-gram-like Language Models Predict Reading Time Best, by James A. Michaelov and Roger P. Levy
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