Papers by Yoshinobu Kawahara

    1 papers
    Timesteps of Mamba Align with Human Reading Times (2026.findings-acl)

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    Challenge: In Mamba, the recurrent state transition at each layer conceptually takes some duration of time, the discretization timestep t, determined dynamically in response to the input.
    Approach: They propose to align per-word processing time in a popular state-space language model Mamba with human reading time using a naturalistic reading dataset.
    Outcome: The proposed model can predict reading times comparable to baselines such as word frequency and GPT-2 surprisal and significant even when they are controlled for.

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