8 papers · 1 filter
Scaling Law: Optimizing Multi-Epoch, Multi-Lingual, and Multi-Stage Training for Low-Resource Language Models
Kosuke Akimoto, Taiki Miyagawa, Masafumi Oyamada
In this paper, we study a fundamental design problem in pretraining Large Language Models (LLMs) for low-resource language regimes. Existing works adopt multi-epoch, multi-lingual,…
Evaluating the Impact of Reviewer Guideline Design on LLM-Based Automated Peer Review
Haowen Li, Yoichi Ishibashi, Masafumi Oyamada
Peer review is an essential process in scientific research, yet the growing workload has made its automation increasingly necessary. In this study, we analyze how different types o…
Can a Crow Hatch a Falcon? Lineage Matters in Predicting Large Language Model Performance
Takuya Tamura, Taro Yano, Masafumi Enomoto +1
Accurately forecasting the performance of Large Language Models (LLMs) before extensive fine-tuning or merging can substantially reduce both computational expense and development t…
An Empirical Study of LLM-as-a-Judge: How Design Choices Impact Evaluation Reliability
Yusuke Yamauchi, Taro Yano, Masafumi Oyamada
As large language models (LLMs) continue to advance, reliable evaluation methods are essential particularly for open-ended, instruction-following tasks. LLM-as-a-Judge enables auto…
Can Large Language Models Invent Algorithms to Improve Themselves?: Algorithm Discovery for Recursive Self-Improvement through Reinforcement Learning
Yoichi Ishibashi, Taro Yano, Masafumi Oyamada
Large Language Models (LLMs) have achieved remarkable capabilities, yet their improvement methods remain fundamentally constrained by human design. We present Self-Developing, a fr…
LaMDAgent: An Autonomous Framework for Post-Training Pipeline Optimization via LLM Agents
Taro Yano, Yoichi Ishibashi, Masafumi Oyamada
Large Language Models (LLMs) have demonstrated exceptional performance across a wide range of tasks. To further tailor LLMs to specific domains or applications, post-training techn…