12 articles tagged with #data-selection. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce the Mimic Score, a geometry-based metric for evaluating data quality in large datasets by measuring gradient alignment with pre-trained models. The proposed Grad-Mimic framework enables efficient data selection, reducing training steps for CLIP models by 20.7% and filtering datasets without expensive computations or validation sets.
Researchers propose a framework for optimizing data selection in large language model instruction tuning by learning task-specific and model-specific weights for multiple quality indicators. Using efficient in-context learning signals on small validation sets, the method achieves comparable performance to full-dataset training with only 30% of samples, revealing important trade-offs between semantic diversity and logical complexity.
Researchers introduce One-Step-Train (OST), a new data selection framework for Large Multimodal Models that uses incremental optimization to identify high-quality training samples. The method reduces computational costs by 43% while outperforming existing approaches like LLM-as-a-Judge, demonstrating significant efficiency gains in multimodal model training.
Researchers introduce SPICE, a data selection algorithm that reduces large language model training data requirements by 90% while maintaining performance by identifying and minimizing gradient conflicts between training samples. The method combines information-theoretic principles with practical efficiency improvements, enabling effective model tuning on just 10% of typical datasets across multiple benchmarks.
Researchers introduce the Data-Model Compatibility (DMC) metric to evaluate how well training datasets align with student models during reasoning distillation from large language models. The metric jointly assesses data quality, difficulty, and student capability, demonstrating strong correlation with distillation performance and enabling dynamic dataset selection that improves outcomes across multiple models and tasks.
Researchers introduce MIRA, a framework for optimizing data selection during mid-training of large language models by dynamically discovering and applying source-specific evaluation rubrics. The approach achieves comparable performance to full-corpus training while reducing token usage by 50% on code-oriented tasks across 21 diverse data sources.
IRDS introduces a new data selection method for reinforcement learning with verifiable rewards (RLVR) that uses sparse autoencoders to identify interpretable, high-value training instances. The approach achieves significant accuracy improvements on math reasoning benchmarks while reducing computational costs by an order of magnitude compared to existing methods.
Researchers introduce SAI-DPO, a dynamic data sampling framework that adapts training data selection based on a model's evolving capabilities during training, rather than using static metrics. Tested on mathematical reasoning benchmarks including AIME24 and AMC23, SAI-DPO achieves state-of-the-art performance with significantly less training data, outperforming baselines by nearly 6 points.
Researchers propose MDS (Multi-turn Dialogue Selection), a framework for improving instruction-tuned language models by intelligently selecting high-quality multi-turn dialogue data. The method combines global coverage analysis with local structural evaluation to filter noisy datasets, demonstrating superior performance across multiple benchmarks compared to existing selection approaches.
Researchers identify a critical flaw in naturalness-based data selection methods for large language model reasoning datasets, where algorithms systematically favor longer reasoning steps rather than higher-quality reasoning. The study proposes two corrective methods (ASLEC-DROP and ASLEC-CASL) that successfully mitigate this 'step length confounding' bias across multiple LLM benchmarks.
Researchers propose CVS, a training-free method for selecting high-quality vision-language training data that requires genuine cross-modal reasoning. The method achieves better performance using only 10-15% of data compared to full dataset training, while reducing computational costs by up to 44%.
Researchers have developed TOSS, a new framework for safely fine-tuning large language models that operates at the token level rather than sample level. The method identifies and removes unsafe tokens while preserving task-specific information, demonstrating superior performance compared to existing sample-level defense methods in maintaining both safety and utility.
