Rethinking Data Selection at Scale: Random Selection is Almost All You
  Need
Authors: name: Tingyu Xia, name: Bowen Yu, name: Kai Dang, name: An Yang, name: Yuan Wu, name: Yuan Tian, name: Yi Chang, name: Junyang Lin

Supervised fine-tuning (SFT) is crucial for aligning Large Language Models
(LLMs) with human instructions. The primary goal during SFT is to select a
small yet representative subset of training data from the larger pool, such
that fine-tuning with this subset achieves results comparable to or even
exceeding those obtained using the entire dataset. However, most existing data
selection techniques are designed for small-scale data pools, which fail to
meet the demands of real-world SFT scenarios. In this paper, we replicated
several self-scoring methods those that do not rely on external model
assistance on two million scale datasets, and found that nearly all methods
struggled to significantly outperform random selection when dealing with such
large-scale data pools. Moreover, our comparisons suggest that, during SFT,
diversity in data selection is more critical than simply focusing on high
quality data. We also analyzed the limitations of several current approaches,
explaining why they perform poorly on large-scale datasets and why they are
unsuitable for such contexts. Finally, we found that filtering data by token
length offers a stable and efficient method for improving results. This
approach, particularly when training on long text data, proves highly
beneficial for relatively weaker base models, such as Llama3.