7 citations · 20 across the 15 of their papers we have counts for
5 papers · 1 filter
Feedback-to-Rubrics: Can We Learn Expert Criteria from Inline Comments?
Kotaro Yoshida, So Kuroki, Yuki Imajuku +4
Large language models (LLMs) are increasingly used for writing and review support, but their usefulness depends on context-dependent criteria, such as expert preferences or organiz…
Open-sci-ref-0.02: open reference baselines for reproducible language model and dataset comparison
Marianna Nezhurina, Harsh Raj, Jörg Franke +7
Reliable progress in language modeling depends on knowing whether a new dataset, architecture, or training recipe is genuinely better than existing alternatives. To foster reproduc…
Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks
Taishi Nakamura, Satoki Ishikawa, Masaki Kawamura +4
Empirical scaling laws have driven the evolution of large language models (LLMs), yet their coefficients shift whenever the model architecture or data pipeline changes. Mixture-of-…
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…
Balancing Speed and Stability: The Trade-offs of FP8 vs. BF16 Training in LLMs
Kazuki Fujii, Taishi Nakamura, Rio Yokota
Large Language Models (LLMs) have attracted significant attention due to their human-like language understanding and generation capabilities, as well as their applicability across…