PPL-Factory: Task-Aware and Budget-Aware Data Selection from Language Modeling to Reasoning
Score
74提出了任务感知与预算感知结合的新视角，但本质上是对已有困惑度方法的改进
实验设计严谨，对比充分，结果显著，论文结构清晰
方法简单、可解释，能显著降低微调计算成本，应用前景广
大模型微调数据选择是当前热点，该工作直接回应效率需求
来自arXiv预印本，属于可信学术来源，且方法复现可验证
Base 82 × T2 Source weight ×0.9 = 74
Curation line 70Selected
Why selected
The method is moderately innovative but the experiments are solid, results are practically valuable, and the paper is clearly written.
AI commentary
「This paper is worth attention as it offers a plug-and-play data selection solution for efficient fine-tuning, especially for research teams and practitioners urgently needing to reduce training costs. Compared to complex selection strategies, PPL-Factory only requires perplexity computation, with low barrier and remarkable results.」
Quick overview
PPL-Factory proposes a data selection framework combining task-aware perplexity scores with data budget-aware criteria for LLM fine-tuning. Experiments show it outperforms existing methods with only 1% training data, and exceeds full-data fine-tuning by 0.9 on GSM8K and 4.8 on MATH with 10% data. The method is simple, interpretable, and effectively reduces fine-tuning computational cost.
- Existing data selection methods rely on indirect heuristics and struggle to generalize across tasks
- PPL-Factory uses task-aware perplexity scores to distinguish language modeling and reasoning objectives
- Incorporates data budget-aware criteria for flexible selection
- Achieves or surpasses full-data fine-tuning performance on GSM8K and MATH with minimal data
Full text
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