Unverified50% confidenceFactExact time
Data engineering also includes sample deduplication (to avoid training set leakage), difficulty stratification (to prevent simple samples from dominating the gradient), and quality filtering (to remove low-quality or harmful content).
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50%
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Long-term
Relevance
10/28/2026
First Seen
Sources
AI Large Model Fine-Tuning Full Process in Practice: Thirteen Lessons from Data to Deployment
bilibili前端vue实战项目10/28/2026
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UnverifiedThe filtering strategy sets a threshold and directly removes low-quality demonstrations; the training set becomes smaller but cleaner, with the problems being threshold selection and the sharp drop in data volume after filtering.75% similarUnverifiedLEAPS adopts a data-contamination-resistant design, with datasets built entirely anew to avoid inflated scores due to models' pretraining memory.75% similarUnverified预处理泄漏在小数据集上尤为显著，当训练集仅有数百张图像时加入测试集样本会明显改变均值和标准差估计74% similarUnverifiedCommon strategies for addressing overfitting on small datasets include data augmentation, regularization techniques (such as Dropout and weight decay), early stopping, and controlling model capacity.74% similar
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