PrivaDE is a privacy-preserving protocol for jointly computing data utility scores in ML using secure computation, with optimizations for efficiency and blockchain integration via smart contracts.
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Data-influence-score filtering using validation-set loss on downstream coding tasks improves Code-LLM performance, with the most beneficial training data varying significantly across different programming tasks.
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PrivaDE: Privacy-preserving Data Evaluation for Blockchain-based Data Marketplaces
PrivaDE is a privacy-preserving protocol for jointly computing data utility scores in ML using secure computation, with optimizations for efficiency and blockchain integration via smart contracts.
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An Empirical Study on Influence-Based Pretraining Data Selection for Code Large Language Models
Data-influence-score filtering using validation-set loss on downstream coding tasks improves Code-LLM performance, with the most beneficial training data varying significantly across different programming tasks.