CDVM formulates data pruning as maximizing total data influence while constraining excessive contributions to any single test point, yielding robust performance on the OpenDataVal benchmark in low-data regimes.
Training data influence analysis and estimation: a survey.Machine Learning, 113(5):2351–2403, March 2024
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PRISM forms predictions as sparse mixtures of learned prototypes trained with clustering objectives, matching dense model accuracy while enabling ~500x faster data attribution and behavior editing without finetuning.
RISE applies CountSketch to dual lexical and semantic channels derived from output-layer gradient outer products, cutting data attribution storage by up to 112x and enabling retrospective and prospective influence analysis on LLMs up to 32B parameters.
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Constraint-Data-Value-Maximization: Utilizing Data Attribution for Effective Data Pruning in Low-Data Environments
CDVM formulates data pruning as maximizing total data influence while constraining excessive contributions to any single test point, yielding robust performance on the OpenDataVal benchmark in low-data regimes.
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Prototype Language Models
PRISM forms predictions as sparse mixtures of learned prototypes trained with clustering objectives, matching dense model accuracy while enabling ~500x faster data attribution and behavior editing without finetuning.
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Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation
RISE applies CountSketch to dual lexical and semantic channels derived from output-layer gradient outer products, cutting data attribution storage by up to 112x and enabling retrospective and prospective influence analysis on LLMs up to 32B parameters.