3 papers
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.CL2026
Garbage Attention in Large Language Models: BOS Sink Heads and Sink-aware Pruning
Jaewon Sok, Jewon Yeom, Seonghyeon Park +2
Large Language Models (LLMs) are known to contain significant redundancy, yet a systematic explanation for why certain components, particularly in higher layers, are more redundant…
cs.CL2026
EpiCaR: Knowing What You Don't Know Matters for Better Reasoning in LLMs
Jewon Yeom, Jaewon Sok, Seonghyeon Park +2
Improving the reasoning abilities of large language models (LLMs) has largely relied on iterative self-training with model-generated data. While effective at boosting accuracy, exi…