AIR：通过自动迭代细化生成复杂指令
AIR: Complex Instruction Generation via Automatic Iterative Refinement
AI总结：
本文提出自动迭代细化框架AIR，通过从文档生成并迭代优化带约束的复杂指令，构建AIR-10K数据集，显著提升大语言模型遵循复杂指令的能力。
摘要：
随着大语言模型的发展，它们遵循简单指令的能力显著提高。然而，遵循复杂指令仍然是一个主要挑战。当前生成复杂指令的方法往往与当前的指令需求不相关，或者存在可扩展性和多样性有限的问题。此外，诸如反向翻译等方法虽然对简单指令生成有效，但未能充分利用大型网络语料库中丰富的内容和结构。在本文中，我们提出了一种新颖的自动迭代细化框架来生成带有约束的复杂指令，这不仅更好地反映了真实场景的需求，还显著增强了LLM遵循复杂指令的能力。AIR框架包括两个阶段：（1）从文档生成初始指令；（2）通过LLM作为评判者的指导，比较模型输出与文档，迭代细化指令以纳入有价值的约束。最后，我们构建了包含10K条复杂指令的AIR-10K数据集，并证明使用我们方法生成的指令显著提高了模型遵循复杂指令的能力，优于现有的指令生成方法。
英文摘要：
With the development of large language models, their ability to follow simple instructions has significantly improved. However, adhering to complex instructions remains a major challenge. Current approaches to generating complex instructions are often irrelevant to the current instruction requirements or suffer from limited scalability and diversity. Moreover, methods such as back-translation, while effective for simple instruction generation, fail to leverage the rich contents and structures in large web corpora. In this paper, we propose a novel automatic iterative refinement framework to generate complex instructions with constraints, which not only better reflects the requirements of real scenarios but also significantly enhances LLMs' ability to follow complex instructions. The AIR framework consists of two stages: (1)Generate an initial instruction from a document; (2)Iteratively refine instructions with LLM-as-judge guidance by comparing the model's output with the document to incorporate valuable constraints. Finally, we construct the AIR-10K dataset with 10K complex instructions and demonstrate that instructions generated with our approach significantly improve the model's ability to follow complex instructions, outperforming existing methods for instruction generation.
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。
