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This is a Quantitative Study study.

October 20, 2025Open Access

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics

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Training on high-quality data selected by our method often outperforms other data selection techniques.
Our technique utilizes training dynamics to measure data quality, enhancing model training across private domains.
We propose a unique quality control evaluation tailored for collaborative settings with heterogeneous domain data.
The technique integrates model merging and federated learning to ensure effective collaboration across different data silos.

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This approach uses training dynamics to select high-quality data in collaborative settings, suggesting enhanced federated learning outcomes.

Zhao et al. (2025) studied this question.

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This approach uses training dynamics to select high-quality data in collaborative settings, suggesting enhanced federated learning outcomes.