Data, Corpora & Tokenization · 2009
The Unreasonable Effectiveness of Data
Argued that for many hard problems, simple models trained on far more data outperform sophisticated models on less, shifting emphasis from clever algorithms to data scale.
Editorial record
Plain-language summary
Drawing on web-scale examples, the authors contend that unreasonably large corpora let comparatively simple methods succeed where elaborate ones with little data fail, and urge researchers to embrace data. Written years before deep learning’s dominance, it foreshadowed the data-centric logic later formalized by scaling laws. It is a conceptual ancestor of the scaling era.
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Antecedents
Conceptual ancestorEvidence: Strongly supported
Scaling Laws for Neural Language Models
Data-centric performance foreshadowed the scaling-law regime
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Provenance
- Record ID
- P-513
- Record created
- 2026-07-13
- Last reviewed
- 2026-07-14
- Record version
- 2
Citation caveat: Citation metadata is approximate and marked unverified in the source dataset.