2 citations · 2 across the 6 of their papers we have counts for
3 papers · 1 filter
Hardware-Native Joint Sparse-Quantization for Trillion-Scale Mixture-of-Experts
Kwanhee Lee, Namhoon Lee, Dan Alistarh
Mixture-of-Experts (MoE) architectures allow frontier language models to scale to trillions of parameters, but their deployment is constrained by massive memory footprints and memo…
Exploiting Local Flatness for Efficient Out-of-Distribution Detection
Seonghwan Park, Hyunji Jung, Dongyeop Lee +1
Detecting out-of-distribution (OOD) data is crucial for reliable machine learning deployment. Among detection strategies, post-hoc methods are particularly attractive due to their…
Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence
Jinseok Chung, Minkyoung Song, Hyunji Jung +1
In-Context Learning (ICL) allows LLMs to adapt to new tasks from a few demonstrations, but its reliability remains a concern: predictions are highly sensitive to both prompt design…