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Minsung Kim

ACL 2026×2ICLR 2026×2可解释性安全多模态社会与人本理论与学习动力学
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2 一作 · 4 篇 · ACL 2026 / ICLR 2026

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2026 顶会论文

4
作者位次 #2/5ICLR 2026Poster· Theory / Interpretability and Visualization

Bilinear representation mitigates reversal curse and enables consistent model editing

Dong-Kyum Kim · Minsung Kim · Jea Kwon · Nakyeong Yang · Meeyoung Cha

可解释性多模态理论与学习动力学OpenReview
一作ACL 2026Poster· Natural Language Processing

How Training Data Shapes the Use of Parametric and In-Context Knowledge in Language Models

Minsung Kim · Dong-Kyum Kim · Jea Kwon · Nakyeong Yang · Kyomin Jung · Meeyoung Cha

摘要

Large language models leverage both parametric knowledge acquired during pretraining and in-context knowledge provided at inference time. Crucially, when these sources conflict, models arbitrate based on their internal confidence, preferring parametric knowledge for high-confidence facts while de...

检索与知识强化学习与后训练效率与系统OpenReview 项目页
一作ACL 2026Findings· Findings of ACL

Rethinking Post-Unlearning Behavior of Large Vision-Language Models

Minsung Kim · Nakyeong Yang · Kyomin Jung

摘要

Large Vision-Language Models (LVLMs) can recognize individuals in images and disclose sensitive personal information about them, raising critical privacy concerns. Machine unlearning aims to remove such knowledge from the model. However, existing methods rarely prescribe what the model should out...

安全多模态推理OpenReview 项目页

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