1 citations · 1 across the 16 of their papers we have counts for
18 papers
Same Trajectory, Contradictory Rewards (ROBORMBENCH): Paraphrase Fragility in Vision Language Reward Models
Wonje Jeung, Sangyeon Yoon, Hyesoo Hong +6
Vision-language models are increasingly used as reward functions for robotic learning, but this role requires paraphrase invariance: the same trajectory should receive the same rew…
VLMs Trace Without Tracking: Diagnosing Failures in Visual Path Following
Hyesoo Hong, Min Soo Kim, Minsoo Kim +4
Vision-language models (VLMs) perform strongly on multimodal benchmarks, yet their ability to reliably follow continuous visual structure remains unclear. We focus on path followin…
Few-Shot Truly Benign DPO Attack for Jailbreaking LLMs
Sangyeon Yoon, Wonje Jeung, Yoonjun Cho +2
Fine-tuning APIs make frontier LLMs easy to customize, but they can also weaken safety alignment during fine-tuning. While prior work shows that benign supervised fine-tuning (SFT)…
BenchPreS: A Benchmark for Context-Aware Personalized Preference Selectivity of Persistent-Memory LLMs
Sangyeon Yoon, Sunkyoung Kim, Hyesoo Hong +5
Large language models (LLMs) increasingly store user preferences in persistent memory to support personalization across interactions. However, in third-party communication settings…
Rethinking Benign Relearning: Syntax as the Hidden Driver of Unlearning Failures
Sangyeon Yoon, Hyesoo Hong, Wonje Jeung +1
Machine unlearning aims to remove specific content from trained models while preserving overall performance. However, the phenomenon of benign relearning, in which forgotten inform…
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…
Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios
Sangyeon Yoon, Wonje Jeung, Albert No
Auditing Differentially Private Stochastic Gradient Descent (DP-SGD) in the final model setting is challenging and often results in empirical lower bounds that are significantly lo…
Large Language Models Still Exhibit Bias in Long Text
Wonje Jeung, Dongjae Jeon, Ashkan Yousefpour +1
Existing fairness benchmarks for large language models (LLMs) primarily focus on simple tasks, such as multiple-choice questions, overlooking biases that may arise in more complex…
ReALFRED: An Embodied Instruction Following Benchmark in Photo-Realistic Environments
Taewoong Kim, Cheolhong Min, Byeonghwi Kim +3
Simulated virtual environments have been widely used to learn robotic agents that perform daily household tasks. These environments encourage research progress by far, but often pr…
An Information Theoretic Evaluation Metric For Strong Unlearning
Dongjae Jeon, Wonje Jeung, Taeheon Kim +2
Machine unlearning (MU) aims to remove the influence of specific data from trained models, addressing privacy concerns and ensuring compliance with regulations such as the ``right…
Learning Equi-angular Representations for Online Continual Learning
Minhyuk Seo, Hyunseo Koh, Wonje Jeung +7
Online continual learning suffers from an underfitted solution due to insufficient training for prompt model update (e.g., single-epoch training). To address the challenge, we prop…