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Rainbow Padding: Mitigating Early Termination in Instruction-Tuned Diffusion LLMs
Bumjun Kim, Dongjae Jeon, Dueun Kim +2
Diffusion large language models (dLLMs) have emerged as a promising alternative to autoregressive models, offering flexible generation orders and strong performance on complex reas…
A2D: Any-Order, Any-Step Safety Alignment for Diffusion Language Models
Wonje Jeung, Sangyeon Yoon, Yoonjun Cho +4
Diffusion large language models (dLLMs) enable any-order generation, but this flexibility enlarges the attack surface: harmful spans may appear at arbitrary positions, and template…
Multi-Level Knowledge Distillation and Dynamic Self-Supervised Learning for Continual Learning
Taeheon Kim, San Kim, Minhyuk Seo +3
Class-incremental with repetition (CIR), where previously trained classes repeatedly introduced in future tasks, is a more realistic scenario than the traditional class incremental…
DUSK: Do Not Unlearn Shared Knowledge
Wonje Jeung, Sangyeon Yoon, Hyesoo Hong +4
Large language models (LLMs) are increasingly deployed in real-world applications, raising concerns about the unauthorized use of copyrighted or sensitive data. Machine unlearning…
R-TOFU: Unlearning in Large Reasoning Models
Sangyeon Yoon, Wonje Jeung, Albert No
Large Reasoning Models (LRMs) embed private or copyrighted information not only in their final answers but also throughout multi-step chain-of-thought (CoT) traces, making reliable…
SEPS: A Separability Measure for Robust Unlearning in LLMs
Wonje Jeung, Sangyeon Yoon, Albert No
Machine unlearning aims to selectively remove targeted knowledge from Large Language Models (LLMs), ensuring they forget specified content while retaining essential information. Ex…
SAFEPATH: Preventing Harmful Reasoning in Chain-of-Thought via Early Alignment
Wonje Jeung, Sangyeon Yoon, Minsuk Kahng +1
Large Reasoning Models (LRMs) have become powerful tools for complex problem solving, but their structured reasoning pathways can lead to unsafe outputs when exposed to harmful pro…
Representation Bending for Large Language Model Safety
Ashkan Yousefpour, Taeheon Kim, Ryan S. Kwon +7
Large Language Models (LLMs) have emerged as powerful tools, but their inherent safety risks - ranging from harmful content generation to broader societal harms - pose significant…