5 citations · 6 across the 9 of their papers we have counts for
11 papers
LLM-jp-4 Technical Report
LLM-jp, :, Chaoran Liu +16
This paper presents LLM-jp-4, a family of Japanese sovereign large language models (LLMs) developed with an emphasis on transparency, reproducibility, and practical usability. LLM-…
Measuring Task-Agnostic Training Data Influence Across Language Model Pretraining
Yuto Nishida, Hirokazu Kiyomaru, Yusuke Oda +6
Measuring training data influence consistently across language model pretraining is challenging. It is difficult to select downstream tasks or validation sets representative of a m…
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…
A Comparative Analysis of LLM Memorization at Statistical and Internal Levels: Cross-Model Commonalities and Model-Specific Signatures
Bowen Chen, Namgi Han, Yusuke Miyao
Memorization is a fundamental component of intelligence for both humans and LLMs. However, while LLM performance scales rapidly, our understanding of memorization lags. Due to limi…
Massive Supervised Fine-tuning Experiments Reveal How Data, Layer, and Training Factors Shape LLM Alignment Quality
Yuto Harada, Yusuke Yamauchi, Yusuke Oda +3
Supervised fine-tuning (SFT) is a critical step in aligning large language models (LLMs) with human instructions and values, yet many aspects of SFT remain poorly understood. We tr…
BIS Reasoning 1.0: The First Large-Scale Japanese Benchmark for Belief-Inconsistent Syllogistic Reasoning
Ha-Thanh Nguyen, Hideyuki Tachibana, Chaoran Liu +4
We present BIS Reasoning 1.0, the first large-scale Japanese dataset of syllogistic reasoning problems explicitly designed to evaluate belief-inconsistent reasoning in large langua…
Do LLMs Need to Think in One Language? Correlation between Latent Language and Task Performance
Shintaro Ozaki, Tatsuya Hiraoka, Hiroto Otake +8
Large Language Models (LLMs) are known to process information using a proficient internal language consistently, referred to as latent language, which may differ from the input or…
How a Bilingual LM Becomes Bilingual: Tracing Internal Representations with Sparse Autoencoders
Tatsuro Inaba, Go Kamoda, Kentaro Inui +5
This study explores how bilingual language models develop complex internal representations. We employ sparse autoencoders to analyze internal representations of bilingual language…
A Statistical and Multi-Perspective Revisiting of the Membership Inference Attack in Large Language Models
Bowen Chen, Namgi Han, Yusuke Miyao
The lack of data transparency in Large Language Models (LLMs) has highlighted the importance of Membership Inference Attack (MIA), which differentiates trained (member) and untrain…
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…
A Multi-Perspective Analysis of Memorization in Large Language Models
Bowen Chen, Namgi Han, Yusuke Miyao
Large Language Models (LLMs), trained on massive corpora with billions of parameters, show unprecedented performance in various fields. Though surprised by their excellent performa…