4 citations · 8 across the 6 of their papers we have counts for
4 papers · 1 filter
Relative Density Ratio Optimization for Stable and Statistically Consistent Model Alignment
Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai +4
Aligning language models with human preferences is essential for ensuring their safety and reliability. Although most existing approaches assume specific human preference models su…
Test-time reward-guided alignment of language models by importance sampling on pre-logit space
Sekitoshi Kanai, Tsukasa Yoshida, Hiroshi Takahashi +2
Test-time alignment of large language models (LLMs) attracts attention because fine-tuning of LLMs requires high computational costs. In this paper, we propose a new test-time rewa…
One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training
Sekitoshi Kanai, Shin'ya Yamaguchi, Masanori Yamada +3
This paper proposes a new loss function for adversarial training. Since adversarial training has difficulties, e.g., necessity of high model capacity, focusing on important data po…
Smoothness Analysis of Adversarial Training
Sekitoshi Kanai, Masanori Yamada, Hiroshi Takahashi +2
Deep neural networks are vulnerable to adversarial attacks. Recent studies about adversarial robustness focus on the loss landscape in the parameter space since it is related to op…