8 papers
Casual as an Anchor: Resolving Supervision Misalignment in Formality Transfer Dataset
Hyojeong Yu, Hyukhun Koh, Minsung Kim +1
Formality transfer is commonly framed as a symmetric bidirectional task between informal and formal registers. We argue that this framing conceals a supervision design flaw in exis…
How Training Data Shapes the Use of Parametric and In-Context Knowledge in Language Models
Minsung Kim, Dong-Kyum Kim, Jea Kwon +3
Large language models leverage both parametric knowledge acquired during pretraining and in-context knowledge provided at inference time. Crucially, when these sources conflict, mo…
Rethinking Post-Unlearning Behavior of Large Vision-Language Models
Minsung Kim, Nakyeong Yang, Kyomin Jung
Large Vision-Language Models (LVLMs) can recognize individuals in images and disclose sensitive personal information about them, raising critical privacy concerns. Machine unlearni…
Erase or Hide? Suppressing Spurious Unlearning Neurons for Robust Unlearning
Nakyeong Yang, Dong-Kyum Kim, Jea Kwon +3
Large language models trained on web-scale data can memorize private or sensitive knowledge, raising significant privacy risks. Although some unlearning methods mitigate these risk…
Bilinear representation mitigates reversal curse and enables consistent model editing
Dong-Kyum Kim, Minsung Kim, Jea Kwon +2
The reversal curse--a language model's inability to infer an unseen fact "B is A" from a learned fact "A is B"--is widely considered a fundamental limitation. We show that this is…
FaithUn: Toward Faithful Forgetting in Language Models by Investigating the Interconnectedness of Knowledge
Nakyeong Yang, Minsung Kim, Seunghyun Yoon +2
Various studies have attempted to remove sensitive or private knowledge from a language model to prevent its unauthorized exposure. However, prior studies have overlooked the compl…