1 citations · 1 across the 4 of their papers we have counts for
4 papers
Distribution-aware Language Neuron Identification in Multilingual Large Language Models
Minjun Kim, Inho Won, Junghun Yuk +3
Multilingual large language models (mLLMs) contain a small fraction of feed-forward neurons that are sensitive to particular languages, commonly termed language-specific neurons. E…
Hidden Threat in Synthetic Data: Covert Targeted Bias Injection through Benign Text
Minkyung Cho, Jihyo Kim, SeungWoo Song +4
Synthetic data is increasingly used to train large language models (LLMs), yet its security implications remain poorly understood. Prior work on subliminal learning suggests that m…
Reflexive Guidance: Improving OoDD in Vision-Language Models via Self-Guided Image-Adaptive Concept Generation
Jihyo Kim, Seulbi Lee, Sangheum Hwang
With the recent emergence of foundation models trained on internet-scale data and demonstrating remarkable generalization capabilities, such foundation models have become more wide…
Deep Active Learning with Contrastive Learning Under Realistic Data Pool Assumptions
Jihyo Kim, Jeonghyeon Kim, Sangheum Hwang
Active learning aims to identify the most informative data from an unlabeled data pool that enables a model to reach the desired accuracy rapidly. This benefits especially deep neu…