3 citations · 8 across the 13 of their papers we have counts for
4 papers · 1 filter
Position: Current Model Cards Are Insufficient for Downstream Governance of Open-Weight Foundation Models
Sungwon Chae, Keonwoo Kim, Hoki Kim +2
The growth of open-weight foundation models (OWFMs) has prompted the AI community to re-evaluate strategies for effective downstream governance. Although model cards have been wide…
Co-occurring Associated REtained concepts in Diffusion Unlearning
Miso Kim, Georu Lee, Yunji Kim +3
Unlearning has emerged as a key technique to mitigate harmful content generation in diffusion models. However, existing methods often remove not only the target concept, but also b…
Machine Unlearning for Masked Diffusion Language Models
Georu Lee, Seungwon Jeong, Hoki Kim +2
Recent masked diffusion language models (MDLMs), such as LLaDA and Dream, have achieved performance comparable to autoregressive large language models. Unlike autoregressive models…
CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios
Taein Lim, Seongyong Ju, Munhyeok Kim +2
Large language models (LLMs) are increasingly deployed as autonomous agents in offensive cybersecurity. In this paper, we reveal an interesting phenomenon: different agents exhibit…