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Found 3 papers, 3 papers with code
Validating Large Language Models with ReLM
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 as data memorization, bias, and inappropriate language.
Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines
Our analysis of over two million ML jobs in Google datacenters reveals that a significant fraction of model training jobs could benefit from faster input data pipelines.
Progressive Compressed Records: Taking a Byte out of Deep Learning Data
Deep learning accelerators efficiently train over vast and growing amounts of data, placing a newfound burden on commodity networks and storage devices.
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