32 citations · 72 across the 21 of their papers we have counts for
4 papers · 1 filter
Jet Expansions of Residual Computation
Yihong Chen, Xiangxiang Xu, Yao Lu +2
We introduce a framework for expanding residual computational graphs using jets, operators that generalize truncated Taylor series. Our method provides a systematic approach to dis…
Quantifying Variance in Evaluation Benchmarks
Lovish Madaan, Aaditya K. Singh, Rylan Schaeffer +5
Evaluation benchmarks are the cornerstone of measuring capabilities of large language models (LLMs), as well as driving progress in said capabilities. Originally designed to make c…
How Data Inter-connectivity Shapes LLMs Unlearning: A Structural Unlearning Perspective
Xinchi Qiu, William F. Shen, Yihong Chen +4
While unlearning knowledge from large language models (LLMs) is receiving increasing attention, one important aspect remains unexplored. Existing approaches and benchmarks assume d…
On the Importance of Strong Baselines in Bayesian Deep Learning
Jishnu Mukhoti, Pontus Stenetorp, Yarin Gal
Like all sub-fields of machine learning Bayesian Deep Learning is driven by empirical validation of its theoretical proposals. Given the many aspects of an experiment it is always…