The effective alignment of Large Language Models (LLMs) with precise instructions is essential for their application in diverse real-world scenarios. Current methods focus on enhancing the diversity and complexity of training and evaluation samples, yet they fall short in accurately assessing LLMs' ability to follow similar instruction variants. We introduce an effective data augmentation technique that decomposes complex instructions into simpler sub-components, modifies these, and reconstructs them into new variants, thereby preserves the original instruction's context and complexity while introducing variability, which is critical for training and evaluating LLMs' instruction-following precision. We developed the DeMoRecon dataset using this method to both fine-tune and evaluate LLMs. Our findings show that LLMs fine-tuned with DeMoRecon will gain significant performance boost on both ours and commonly used instructions-following benchmarks.

翻译：大型语言模型（LLMs）与精确指令的有效对齐对其在多样化现实场景中的应用至关重要。当前方法侧重于增强训练与评估样本的多样性和复杂性，但未能准确评估LLMs遵循相似指令变体的能力。我们提出一种高效的数据增强技术，将复杂指令分解为更简单的子组件，对其进行修改，并重构为新的变体。该方法在保持原始指令上下文与复杂度的同时引入可变性，这对训练和评估LLMs的指令遵循精度至关重要。我们运用此方法构建了DeMoRecon数据集，用于对LLMs进行微调和评估。实验结果表明，使用DeMoRecon微调的LLMs在我们构建的测试集及常用指令遵循基准上均获得显著的性能提升。