10 citations · 36 across the 26 of their papers we have counts for
6 papers · 1 filter
Compositional Generalization via Structural Identification in a Category-Theoretic Framework
Akihiro Maeda, Thomas Seiller, Yohei Oseki
Compositional generalization is usually evaluated through model accuracy. We instead ask which structural or lexical identifications make held-out COGS examples admissible from the…
JSL-DC: A Word-Level Japanese Sign Language Dataset with Linguist-Derived Descriptions for Distinguishing Confusable Signs
Ken Takaki, Asuka Ando, Misa Suzuki +12
Effective sign language (SL) acquisition is crucial for deaf children, yet 95% are born to hearing parents who often lack proficiency in SL. SL recognition can power learning tools…
Language Acquisition Device in Large Language Models
Masato Mita, Taiga Someya, Ryo Yoshida +1
Large Language Models (LLMs) remain substantially less data-efficient than humans. Pre-pretraining (PPT) on synthetic languages has been proposed to close this gap, with prior work…
Dual Alignment Between Language Model Layers and Human Sentence Processing
Tatsuki Kuribayashi, Alex Warstadt, Yohei Oseki +1
A recent study (Kuribayashi et al., 2025) has shown that human sentence processing behavior, typically measured on syntactically unchallenging constructions, can be effectively mod…
An Existence Proof for Neural Language Models That Can Explain Garden-Path Effects via Surprisal
Ryo Yoshida, Shinnosuke Isono, Taiga Someya +2
Surprisal theory hypothesizes that the difficulty of human sentence processing increases linearly with surprisal, the negative log-probability of a word given its context. Computat…
Exclusive Unlearning
Mutsumi Sasaki, Kouta Nakayama, Yusuke Miyao +2
When introducing Large Language Models (LLMs) into industrial applications, such as healthcare and education, the risk of generating harmful content becomes a significant challenge…