1 citations · 1 across the 8 of their papers we have counts for
3 papers · 1 filter
GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks
Tejal Patwardhan, Rachel Dias, Elizabeth Proehl +16
We introduce GDPval, a benchmark evaluating AI model capabilities on real-world economically valuable tasks. GDPval covers the majority of U.S. Bureau of Labor Statistics Work Acti…
Energy Consumption in Parallel Neural Network Training
Philipp Huber, David Li, Juan Pedro Gutiérrez Hermosillo Muriedas +4
The increasing demand for computational resources of training neural networks leads to a concerning growth in energy consumption. While parallelization has enabled upscaling model…
Large Language Model Compression via the Nested Activation-Aware Decomposition
Jun Lu, Tianyi Xu, Bill Ding +2
In this paper, we tackle the critical challenge of compressing large language models (LLMs) to facilitate their practical deployment and broader adoption. We introduce a novel post…