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20242026
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cs.CL2026

Human-LLM Alignment in Language Attitudes Toward Non-Native Japanese

Naho Orita, Hayato Ogawa, Daisuke Kawahara

Large language models (LLMs) increasingly evaluate human writing in high-stakes domains such as hiring and academic assessment, putting non-native speakers at particular risk. Draw…

cs.CL2026

Detecting Sensitive Personal Information in Japanese Pre-Training Corpora for Large Language Models

Rei Minamoto, Yusuke Oda, Daisuke Kawahara

Sensitive personal information can appear in large-scale pre-training corpora for large language models (LLMs). Detecting and filtering such information is therefore essential to e…

cs.CL2026

ShapleyLaw: A Game-Theoretic Approach to Multilingual Scaling Laws

Xuyang Cao, Qianying Liu, Chuan Xiao +7

In multilingual pretraining, the test loss of a pretrained model is heavily influenced by the proportion of each language in the pretraining data, namely the \textit{language mixtu…

cs.CL2024

LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs

LLM-jp, :, Akiko Aizawa +80

This paper introduces LLM-jp, a cross-organizational project for the research and development of Japanese large language models (LLMs). LLM-jp aims to develop open-source and stron…

cs.CL2024

Constructing Multimodal Datasets from Scratch for Rapid Development of a Japanese Visual Language Model

Keito Sasagawa, Koki Maeda, Issa Sugiura +3

To develop high-performing Visual Language Models (VLMs), it is essential to prepare multimodal resources, such as image-text pairs, interleaved data, and instruction data. While m…