通过标签空间控制指令复杂度，提升大模型训练数据质量。
TAG-INSTRUCT: Controlled Instruction Complexity Enhancement through Structure-based Augmentation
- 将指令压缩到标签空间，实现复杂度可控的结构化增强
- 基于强化学习扩展标签，显著提升指令难度与多样性
- 适合需要精细调控训练数据复杂度的研究者
高质量指令数据对大语言模型的发展至关重要，但现有方法难以有效控制指令复杂度。本文提出TAG-INSTRUCT，一种通过结构化语义压缩和可控难度增强来提升指令复杂度的新框架。不同于以往在原始文本上操作的提示方法，TAG-INSTRUCT将指令压缩至紧凑的标签空间，并通过强化学习引导的标签扩展系统性地提升复杂度。大量实验表明，TAG-INSTRUCT优于现有指令复杂度增强方法。分析显示，在标签空间中操作具有更强的可控性和跨不同指令生成框架的稳定性。
原文摘要 · Abstract (English)
High-quality instruction data is crucial for developing large language models (LLMs), yet existing approaches struggle to effectively control instruction complexity. We present TAG-INSTRUCT, a novel framework that enhances instruction complexity through structured semantic compression and controlled difficulty augmentation. Unlike previous prompt-based methods operating on raw text, TAG-INSTRUCT compresses instructions into a compact tag space and systematically enhances complexity through RL-guided tag expansion. Through extensive experiments, we show that TAG-INSTRUCT outperforms existing instruction complexity augmentation approaches. Our analysis reveals that operating in tag space provides superior controllability and stability across different instruction synthesis frameworks.
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