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cs.LG2026
Efficient Epistemic Uncertainty Estimation for Large Language Models via Knowledge Distillation
Seonghyeon Park, Jewon Yeom, Jaewon Sok +3
Quantifying uncertainty in Large Language Models (LLMs) is essential for mitigating hallucinations and enabling risk-aware deployment in safety-critical tasks. However, estimating…
cs.LG2026
Stable On-Policy Distillation through Adaptive Target Reformulation
Ijun Jang, Jewon Yeom, Juan Yeo +2
Knowledge distillation (KD) is a widely adopted technique for transferring knowledge from large language models to smaller student models; however, conventional supervised KD often…
cs.LG2025
Generalized and Personalized Federated Learning with Black-Box Foundation Models via Orthogonal Transformations
Eun Gyung Kong, Je Won Yeom, Yonghoon Jeon +1
Federated Learning (FL) facilitates decentralized model training while preserving data privacy. However, achieving both robust generalization and effective personalization simultan…