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The One Billion Word Benchmark is a dataset derived from the WMT 2011 NewsCrawl, commonly used to measure language modeling ability in natural languageprocessing. We train models solely on Common Crawl web scrapes partitioned byyear, and demonstrate that they perform worse on this task over time due todistributional shift. Analysis of this corpus reveals that it contains severalexamples of harmful text, as well as outdated references to current events. Wesuggest that the temporal nature of news and its distribution shift over timemakes it poorly suited for measuring language modeling ability, and discusspotential impact and considerations for researchers building language modelsand evaluation datasets.

No News is Good News: A Critique of the One Billion Word Benchmark