961 citations · 1.5k across the 36 of their papers we have counts for
18 papers · 1 filter
Optimizing LLM Queries in Relational Data Analytics Workloads
Shu Liu, Asim Biswal, Amog Kamsetty +8
Batch data analytics is a growing application for Large Language Models (LLMs). LLMs enable users to perform a wide range of natural language tasks, such as classification, entity…
FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance
Lingjiao Chen, Matei Zaharia, James Zou
There is a rapidly growing number of large language models (LLMs) that users can query for a fee. We review the cost associated with querying popular LLM APIs, e.g. GPT-4, ChatGPT,…
MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Trevor Gale, Deepak Narayanan, Cliff Young +1
We present MegaBlocks, a system for efficient Mixture-of-Experts (MoE) training on GPUs. Our system is motivated by the limitations of current frameworks, which restrict the dynami…
What can Data-Centric AI Learn from Data and ML Engineering?
Neoklis Polyzotis, Matei Zaharia
Data-centric AI is a new and exciting research topic in the AI community, but many organizations already build and maintain various "data-centric" applications whose goal is to pro…
DistIR: An Intermediate Representation and Simulator for Efficient Neural Network Distribution
Keshav Santhanam, Siddharth Krishna, Ryota Tomioka +2
The rapidly growing size of deep neural network (DNN) models and datasets has given rise to a variety of distribution strategies such as data, tensor-model, pipeline parallelism, a…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…