3 papers
cs.LG2026
Rewriting Pre-Training Data Boosts LLM Performance in Math and Code
Kazuki Fujii, Yukito Tajima, Sakae Mizuki +14
The performance of large language models (LLMs) in program synthesis and mathematical reasoning is fundamentally limited by the quality of their pre-training corpora. We introduce…
cs.CL2025
Why We Build Local Large Language Models: An Observational Analysis from 35 Japanese and Multilingual LLMs
Koshiro Saito, Sakae Mizuki, Masanari Ohi +11
Why do we build local large language models (LLMs)? What should a local LLM learn from the target language? Which abilities can be transferred from other languages? Do language-spe…
cs.CL2025
Building Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models
Youmi Ma, Sakae Mizuki, Kazuki Fujii +12
Instruction tuning is crucial for enabling Large Language Models (LLMs) to solve real-world tasks. Prior work has shown the effectiveness of instruction-tuning data synthesized sol…