Take the essence and discard the dross: A Rethinking on Data Selection for Fine-Tuning Large Language Models
This work addresses the problem of inconsistent comparisons in data selection research for fine-tuning LLMs, offering a systematic review and framework, but it is incremental as it builds on existing surveys without new empirical results.
The paper tackles the lack of a unified framework for data selection in fine-tuning large language models by reviewing recent techniques and introducing a three-stage scheme for categorization and evaluation, finding that methods with targeted quality measurement achieve higher efficiency but lower feasibility.
Data selection for fine-tuning large language models (LLMs) aims to choose a high-quality subset from existing datasets, allowing the trained model to outperform baselines trained on the full dataset. However, the expanding body of research lacks a clear, unified framework, and the variability in experimental settings complicates systematic comparisons. While existing surveys comprehensively overview the stages and methods of data selection, they often overlook an in-depth exploration of the fine-tuning phase. In this paper, we conduct a focused review of recent data selection techniques for fine-tuning LLMs, analyzing a dozen key studies. We introduce a novel three-stage scheme - comprising feature extraction, criteria design, and selector evaluation - to systematically categorize and evaluate these methods. Additionally, we propose a unified comparison approach that incorporates ratio-based efficiency and ranking-based feasibility metrics to address inconsistencies across experiments. Our findings reveal that methods emphasizing more targeted quality measurement achieve higher efficiency but at the cost of feasibility. Finally, we discuss trends and highlight four key challenges in fine-tuning data selection, offering potential directions for future research.