activity
20242026
collaborators

16 papers

cs.CV2026

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…

cs.CL2026

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…

cs.CL2026

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…

cs.CL2026

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…

cs.CL2026

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…

cs.CL2026

Rethinking the Relationship between the Power Law and Hierarchical Structures

Kai Nakaishi, Ryo Yoshida, Kohei Kajikawa +2

Statistical analysis of corpora provides an approach to quantitatively investigate natural languages. This approach has revealed that several power laws consistently emerge across…