3 papers
cs.CL2025
Automated Refinement of Essay Scoring Rubrics for Language Models via Reflect-and-Revise
Keno Harada, Lui Yoshida, Takeshi Kojima +2
The performance of Large Language Models (LLMs) is highly sensitive to the prompts they are given. Drawing inspiration from the field of prompt optimization, this study investigate…
cs.CL2025
When Instructions Multiply: Measuring and Estimating LLM Capabilities of Multiple Instructions Following
Keno Harada, Yudai Yamazaki, Masachika Taniguchi +4
As large language models (LLMs) are increasingly applied to real-world scenarios, it becomes crucial to understand their ability to follow multiple instructions simultaneously. To…
cs.LG2024
ADOPT: Modified Adam Can Converge with Any with the Optimal Rate
Shohei Taniguchi, Keno Harada, Gouki Minegishi +7
Adam is one of the most popular optimization algorithms in deep learning. However, it is known that Adam does not converge in theory unless choosing a hyperparameter, i.e., ,…