IterSelectTune：一种用于高效指令微调数据选择的迭代训练框架
IterSelectTune: An Iterative Training Framework for Efficient Instruction-Tuning Data Selection
AI总结：
本文提出IterSelectTune迭代训练框架，在几乎不依赖人工和GPT-4的情况下，仅用约20%源数据选择高质量指令数据，并在多个基准上超越全量数据微调效果，从而降低指令微调的计算成本。
摘要：
随着大语言模型（LLMs）的持续进步，指令微调对于提升其生成准确且符合上下文回答的能力已变得至关重要。尽管已有大量指令微调数据集被开发出来以增强LLM性能，但从大型源数据集中挑选高质量指令数据通常需要大量人工投入。在本工作中，我们提出了$\ extbf{IterSelectTune}$，这是一种高效、低成本且无需人工参与、对GPT-4依赖有限的迭代训练策略，用于选择高质量指令数据。通过在约20%的源数据上进行微调，我们的方法在多个基准和公开测试数据集上持续优于在完整数据集上微调的模型。这些结果凸显了我们的方法在提升LLM性能的同时，能够减少指令微调所需的计算资源。
英文摘要：
As large language models (LLMs) continue to advance, instruction tuning has become critical for improving their ability to generate accurate and contextually appropriate responses. Although numerous instruction-tuning datasets have been developed to enhance LLM performance, selecting high-quality instruction data from large source datasets typically demands significant human effort. In this work, we introduce $\textbf{IterSelectTune}$, an efficient, cost-effective iterative training policy for selecting high-quality instruction data with no human involvement and limited reliance on GPT-4. By fine-tuning on approximately 20\% of the source data, our method consistently outperforms models fine-tuned on the full dataset across multiple benchmarks and public test datasets. These results highlight the effectiveness of our approach in enhancing LLM performance while reducing the computational resources required for instruction tuning.
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