筛选高质量指令数据的策略其实不如随机选，效果不稳定。
Chasing Random: Instruction Selection Strategies Fail to Generalize
- 对比多种数据筛选策略在不同数据集上的表现
- 多数策略表现不如随机抽样，增益微乎其微
- 筛选成本常高于全量微调，性价比不高
先前研究表明，仅用少量高质量指令即可微调语言模型以遵循用户指令。这推动了从大量噪声指令数据中筛选优质子集的方法发展。然而，这些方法通常未在统一实验设置下评估，其泛化能力不明确。本文分析了主流选择策略在不同源数据集、筛选预算和评估基准下的表现：结果表明，这些策略泛化能力差，往往无法持续优于随机基线。我们还分析了数据筛选的成本-性能权衡，发现筛选成本常超过对全量数据微调的开销，但带来的性能提升却微小甚至无益。
原文摘要 · Abstract (English)
Prior work has shown that language models can be tuned to follow user instructions using only a small set of high-quality instructions. This has accelerated the development of methods that filter a large, noisy instruction-tuning datasets down to high-quality subset which works just as well. However, typically, the performance of these methods is not demonstrated across a uniform experimental setup and thus their generalization capabilities are not well established. In this work, we analyze popular selection strategies across different source datasets, selection budgets and evaluation benchmarks: Our results indicate that selection strategies generalize poorly, often failing to consistently outperform even random baselines. We also analyze the cost-performance trade-offs of using data selection. Our findings reveal that data selection can often exceed the cost of fine-tuning on the full dataset, yielding only marginal and sometimes no gains compared to tuning on the full dataset or a random subset.
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