Data selection vs. filtering: why generic filters fail at scale
giffmana · x · 2026-08-23
Discussion on data processing suggests framing the problem as "data selection" rather than "data filtering". In general-purpose model settings, with sufficient compute, nearly all data becomes useful. However, for specific practical use cases, selectors targeting specific distributions are more effective than generic filters. Smart filters should be avoided unless compute is limited or a general model is not the goal.
Related event: Filtering vs. Selection: Debating Training Data Preprocessing Strategies(5 posts)→
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