18 citations · 56 across the 7 of their papers we have counts for
7 papers
A Standardized Machine-readable Dataset Documentation Format for Responsible AI
Nitisha Jain, Mubashara Akhtar, Joan Giner-Miguelez +15
Data is critical to advancing AI technologies, yet its quality and documentation remain significant challenges, leading to adverse downstream effects (e.g., potential biases) in AI…
Introducing v0.5 of the AI Safety Benchmark from MLCommons
Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed +97
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safe…
Croissant: A Metadata Format for ML-Ready Datasets
Mubashara Akhtar, Omar Benjelloun, Costanza Conforti +28
Data is a critical resource for machine learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that crea…
Validating Large Language Models with ReLM
Michael Kuchnik, Virginia Smith, George Amvrosiadis
Although large language models (LLMs) have been touted for their ability to generate natural-sounding text, there are growing concerns around possible negative effects of LLMs such…
Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines
Michael Kuchnik, Ana Klimovic, Jiri Simsa +2
Input pipelines, which ingest and transform input data, are an essential part of training Machine Learning (ML) models. However, it is challenging to implement efficient input pipe…
Progressive Compressed Records: Taking a Byte out of Deep Learning Data
Michael Kuchnik, George Amvrosiadis, Virginia Smith
Deep learning accelerators efficiently train over vast and growing amounts of data, placing a newfound burden on commodity networks and storage devices. A common approach to conser…
Efficient Augmentation via Data Subsampling
Michael Kuchnik, Virginia Smith
Data augmentation is commonly used to encode invariances in learning methods. However, this process is often performed in an inefficient manner, as artificial examples are created…