7 citations · 19 across the 13 of their papers we have counts for
3 papers · 1 filter
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…
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…
LCTG Bench: LLM Controlled Text Generation Benchmark
Kentaro Kurihara, Masato Mita, Peinan Zhang +3
The rise of large language models (LLMs) has led to more diverse and higher-quality machine-generated text. However, their high expressive power makes it difficult to control outpu…