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.

翻译：大型语言模型（LLMs）已深刻影响写作过程，促进了协同内容创作并提升了生产效率。然而，生成高质量且符合用户意图的文本仍充满挑战。本文提出Writing Path框架，通过显式大纲引导LLMs生成目标导向的高质量写作文本。该方法借鉴结构化写作规划与推理路径思想，聚焦于在写作过程中捕捉并反映用户意图。我们基于非结构化博客文章构建多样化数据集以评估写作性能，并引入综合评估框架对大纲及生成文本质量进行评判。基于GPT-3.5-turbo、GPT-4及HyperCLOVA X的评估表明，Writing Path方法在LLMs评估与人工评估中均显著提升文本质量。本研究凸显了将写作专项技术融入LLMs以增强其满足用户多样化写作需求的潜力。