implementation detail · filed under data curation
PCA-based dimension selection for decorrelated quality signals
A PCA-informed subset of quality dimensions is selected to decorrelate signals before forming a composite score.
Also called PCA-informed subset of its dimensions.
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Evidence
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We keep Propella’s native rating ranges, use a PCA-informed subset of its dimensions to de-correlate signals, and then form the composite score
useddata curationin Laguna XS.2Poolside
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Other methods under data curation :: data filtering.
CSAM filteringSensitive data filteringCBRN pre-training data filteringFiltering batched auto-generated and templated contentHeuristic filteringKeyword- and regex-based filteringModel-based refusal and inability filteringPass@100 filteringPathological repetition filteringSmolVLM-based image-text quality scoringUnified data filtering pipelineAutomated verificationCoding-agent session filtering and trajectory deduplicationConservative model-based noise filteringContent quality and safety filteringContinuous contribution-score rankingData cleaningData filtering pipelineDense annotation of ambiguous low-quality dataDomain filtering with heuristics, classifier scoring, and deduplicationEvidence-based data cleaning and training constraintsFiltering low-information model-generated contentHeuristic and statistical filtering with deduplication and quality modelsImage-text relevance filtering