Expired65% confidenceOpinionExact time
The quality of fine-tuning data is far more important than quantity; typically, a few hundred to a few thousand high-quality annotated data samples can significantly improve a model's performance on a specific task.
3
Sources
65%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026(expired)
Sources
Related Claims
Unverified业界普遍认为数百条高质量标注准确的领域样本其微调效果可能优于数千条质量参差不齐的样本77% similarUnverified靶向数据工程中,针对性数据占总微调数据的 5%-15% 通常能取得良好效果,过高比例可能导致模型在其他维度退化75% similarUnverifiedIndustry experience shows that data quality is far more important than quantity in the SFT stage, and a small number of carefully annotated high-quality samples often outperforms a large number of low-quality samples.72% similarUnverifiedA carefully curated and deduplicated high-quality dataset can train a model of equal or even superior capability using far fewer tokens than the original scale.72% similarUnverifiedResearch shows that, for the same task, an optimized prompt can improve model output quality by 30%–50% compared to arbitrary input.71% similar
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