使用细粒度指令变体增强与评估指令遵循能力
Enhancing and Assessing Instruction-Following with Fine-Grained Instruction Variants
AI总结：
针对LLMs难以准确评估和遵循相似指令变体的问题，提出数据增强技术DeMoRecon构建细粒度指令变体数据集FGIV，经其微调的模型在多种指令遵循基准上性能显著提升。
摘要：
大型语言模型（LLMs）与精确指令的有效对齐对于其在多样化真实世界场景中的应用至关重要。当前方法侧重于提升训练和评估样本的多样性与复杂性，但在准确评估LLMs遵循相似指令变体的能力方面存在不足。我们引入了一种有效的数据增强技术DeMoRecon，该技术将复杂指令分解为更简单的子组件，对其进行修改，并重构为新变体，从而在保留原始指令上下文和复杂性的同时引入变异性，这对于训练和评估LLMs的指令遵循精度至关重要。基于DeMoRecon，我们开发了FGIV数据集，其中包含1,773个种子指令的细粒度指令变体，用于微调和评估LLMs。我们的研究结果表明，使用FGIV微调的LLMs在我们及常用的指令遵循基准上均获得了显著的性能提升。
英文摘要：
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 DeMoRecon 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. Based on DeMoRecon, we developed the FGIV dataset which contains fine-grained instruction variants of 1,773 seed instructions to both fine-tune and evaluate LLMs. Our findings show that LLMs fine-tuned with FGIV will gain significant performance boost on both ours and commonly used instructions-following benchmarks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
