1.2k citations · 1.3k across the 4 of their papers we have counts for
4 papers
Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
Bradley Brown, Jordan Juravsky, Ryan Ehrlich +4
Scaling the amount of compute used to train language models has dramatically improved their capabilities. However, when it comes to inference, we often limit models to making only…
Scaling Instruction-Finetuned Language Models
Hyung Won Chung, Le Hou, Shayne Longpre +32
Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we expl…
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
David Patterson, Joseph Gonzalez, Urs Hölzle +7
Machine Learning (ML) workloads have rapidly grown in importance, but raised concerns about their carbon footprint. Four best practices can reduce ML training energy by up to 100x…
A Full-Stack Search Technique for Domain Optimized Deep Learning Accelerators
Dan Zhang, Safeen Huda, Ebrahim Songhori +4
The rapidly-changing deep learning landscape presents a unique opportunity for building inference accelerators optimized for specific datacenter-scale workloads. We propose Full-st…