Large Language Models (LLMs) have significantly impacted the writing process,
enabling collaborative content creation and enhancing productivity. However,
generating high-quality, user-aligned text remains challenging. In this paper,
we propose Writing Path, a framework that uses explicit outlines to guide LLMs
in generating goal-oriented, high-quality pieces of writing. Our approach draws
inspiration from structured writing planning and reasoning paths, focusing on
capturing and reflecting user intentions throughout the writing process. We
construct a diverse dataset from unstructured blog posts to benchmark writing
performance and introduce a comprehensive evaluation framework assessing the
quality of outlines and generated texts. Our evaluations with GPT-3.5-turbo,
GPT-4, and HyperCLOVA X demonstrate that the Writing Path approach
significantly enhances text quality according to both LLMs and human
evaluations. This study highlights the potential of integrating
writing-specific techniques into LLMs to enhance their ability to meet the
diverse writing needs of users.

通过引入 Writing Path 框架，本研究提出了一种利用明确的大纲来指导大型语言模型生成目标导向、高质量写作的方法。在使用 GPT-3.5-turbo、GPT-4 和 HyperCLOVA X 进行评估时，该方法显著提高了文本质量，进而提升了大型语言模型满足用户多样化写作需求的能力。