5 papers
Semantic-aware Wasserstein Policy Regularization for Large Language Model Alignment
Byeonghu Na, Hyungho Na, Yeongmin Kim +4
Large language models (LLMs) are commonly aligned with human preferences using reinforcement learning from human feedback (RLHF). In this method, LLM policies are generally optimiz…
Prompt-Based Safety Guidance Is Ineffective for Unlearned Text-to-Image Diffusion Models
Jiwoo Shin, Byeonghu Na, Mina Kang +2
Recent advances in text-to-image generative models have raised concerns about their potential to produce harmful content when provided with malicious input text prompts. To address…
Training-Free Safe Text Embedding Guidance for Text-to-Image Diffusion Models
Byeonghu Na, Mina Kang, Jiseok Kwak +6
Text-to-image models have recently made significant advances in generating realistic and semantically coherent images, driven by advanced diffusion models and large-scale web-crawl…
Diffusion Adaptive Text Embedding for Text-to-Image Diffusion Models
Byeonghu Na, Minsang Park, Gyuwon Sim +6
Text-to-image diffusion models rely on text embeddings from a pre-trained text encoder, but these embeddings remain fixed across all diffusion timesteps, limiting their adaptabilit…
Preference Optimization by Estimating the Ratio of the Data Distribution
Yeongmin Kim, Heesun Bae, Byeonghu Na +1
Direct preference optimization (DPO) is widely used as a simple and stable method for aligning large language models (LLMs) with human preferences. This paper investigates a genera…