Papers by Wonje Jeung
SEPS: A Separability Measure for Robust Unlearning in LLMs (2025.emnlp-main)
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| Challenge: | Existing unlearning metrics assess whether a model correctly answers retain queries and rejects forget queries, but they fail to capture real-world scenarios where forget queries rarely appear in isolation. |
| Approach: | They propose an evaluation framework that explicitly measures a model’s ability to both forget and retain information within a single prompt. |
| Outcome: | The proposed approach significantly improves unlearning effectiveness, demonstrating robustness even in complex settings with up to eight mixed forget and retain queries in a single prompt. |
Representation Bending for Large Language Model Safety (2025.acl-long)
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Ashkan Yousefpour, Taeheon Kim, Ryan Sungmo Kwon, Seungbeen Lee, Wonje Jeung, Seungju Han, Alvin Wan, Harrison Ngan, Youngjae Yu, Jonghyun Choi
| Challenge: | Existing safety-enhancing techniques, such as fine-tuning with human feedback or adversarial training, are still vulnerable as they address specific threats and fail to generalize across unseen attacks. |
| Approach: | They propose a new approach that disrupts representations underlying harmful behaviors in Large Language Models by using loss-based fine-tuning. |
| Outcome: | The proposed approach outperforms existing methods such as Circuit Breaker, RMU, and NPO with 95% reduction in attack success rates across diverse jailbreak benchmarks. |
Large Language Models Still Exhibit Bias in Long Text (2025.findings-acl)
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| Challenge: | Existing fairness benchmarks for large language models focus on simple tasks . a new framework evaluates biases in LLMs through essay-style prompts . |
| Approach: | They propose a framework that evaluates biases in large language models through essay-style prompts. |
| Outcome: | The proposed framework uncovers subtle biases difficult to detect in simple responses. |
R-TOFU: Unlearning in Large Reasoning Models (2025.emnlp-main)
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| Challenge: | Large Reasoning Models embed private or copyrighted information in their final answers and throughout multi-step chain-of-thought (CoT) traces. |
| Approach: | They propose a benchmark for Large Reasoning Models that augments existing unlearning tasks with realistic CoT annotations and step-wise metrics that expose residual knowledge invisible to answer-level checks. |
| Outcome: | The proposed benchmark shows that answer-only objectives leave substantial forget traces in reasoning. |
DUSK: Do Not Unlearn Shared Knowledge (2026.findings-acl)
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| Challenge: | Recent work suggests that machine learning models are indistinguishable from models trained on retain sets. |
| Approach: | They propose a benchmark to evaluate machine unlearning under realistic knowledge overlap . they construct documents containing both shared and unique knowledge . |
| Outcome: | The proposed model is indistinguishable from a model retrained on the retain set while only forget-specific content is removed. |