Author

Kyle Mahowald

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Preprint Oct 2026

Why is AI-generated language so hard to understand?

There is a widespread sense that AI-generated text has recently become harder to understand. In this study, we investigate a set of psycholinguistic features that are known to correlate with human processing difficulty (e.g., object relative clauses, long syntactic dependencies, low-frequency n-grams). Using a novel co...

Thomas Hikaru Clark, Matias B. Martin, Kyle Mahowald · 0 citations

#artificial intelligence Preprint Oct 2026

Vision-Language Models Integrate Conflicting Evidence Like Biased Bayesian Observers

Vision-language models must reconcile conflicting text and images. Prior work has characterized the resulting behavior in terms of modality biases--e.g., a"blind faith in text"--but why preferences vary across models, domains, and input conditions remains unclear. We develop a predictive account from a Bayesian perspec...

J. Barua, Kyle Mahowald, William Rudman · 0 citations

#natural language process... Preprint Aug 2026

Causal Interventions Reveal Typologically Organized Syntactic Mechanisms in Multilingual Language Models

Linguistic theory has long recognized cross-linguistic syntactic regularities, leading to claims that these similar structures are processed by similar mechanisms. However, this hypothesis has been difficult to test empirically due to our lack of fine-grained, manipulable access of human processing mechanisms. In this...

Sasha Boguraev, Toshiki Nakai, Kyle Mahowald et al. · 0 citations