9 papers · 1 filter
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…
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…
Llama-Mimi: Exploring the Limits of Flattened Speech Language Modeling
Issa Sugiura, Shuhei Kurita, Yusuke Oda +1
Speech Language Models (SpeechLMs) model tokenized speech to capture both semantic and acoustic information. When neural audio codecs based on Residual Vector Quantization (RVQ) ar…
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…
Instability in Downstream Task Performance During LLM Pretraining
Yuto Nishida, Masaru Isonuma, Yusuke Oda
When training large language models (LLMs), it is common practice to track downstream task performance throughout the training process and select the checkpoint with the highest va…
llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length
Issa Sugiura, Kouta Nakayama, Yusuke Oda
Encoder-only transformer models like BERT are widely adopted as a pre-trained backbone for tasks like sentence classification and retrieval. However, pretraining of encoder models…