In order to train more powerful large language models, researchers use vast dataset collections that blend diverse data from thousands of web sources. But as these datasets are combined and recombined into multiple collections, important information about their origins and restrictions on how they can be used are often lost or confounded in the shuffle.
Transparency is often lacking in datasets used to train large language models, study finds
Reader’s Picks
-
“Birthrates are plummeting worldwide. Can governments turn the tide?” “The world is running out of children as global birth rates [...]
-
Scotland’s care system is taking years to find many of the country’s most vulnerable children permanent homes—and too many of [...]
-
Social media is negatively impacting the life satisfaction of Australian high school students, according to the latest findings from Australia’s [...]