14 papers
Personalized Privacy Control in LLMs via Attention Head Intervention
Junseok Kim, Nakyeong Yang, Kyomin Jung
The rise of agentic AI enables LLMs to access diverse user data, raising critical privacy concerns. Prior work on contextual privacy studies whether LLMs regulate information discl…
Are We Measuring Strategy or Phrasing? The Gap Between Surface- and Approach-Level Diversity in LLM Math Reasoning
Sangmook Lee, Minbeom Kim, Jeonghye Kim +3
Diversity in LLM mathematical reasoning is critical for exploration, but common diversity metrics mostly capture surface-level variation rather than differences in how a problem is…
Beyond Normalization: Rethinking the Partition Function as a Difficulty Scheduler for RLVR
Dohyung Kim, Minbeom Kim, Jeonghye Kim +3
Reward-maximizing RL methods have shown to be capable of enhancing the reasoning performance of LLMs, but often lead to reduced generation diversity. Recent works address this issu…
Reliability-Aware Adaptive Self-Consistency for Efficient Sampling in LLM Reasoning
Junseok Kim, Nakyeong Yang, Kyungmin Min +1
Self-Consistency improves reasoning reliability through multi-sample aggregation, but incurs substantial inference cost. Adaptive self-consistency methods mitigate this issue by ad…
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…