Navigating the Path of Writing: Outline-guided Text Generation with Large Language Models

• URL: http://arxiv.org/abs/2404.13919v1
• Date: Mon, 22 Apr 2024 06:57:43 GMT
• Title: Navigating the Path of Writing: Outline-guided Text Generation with Large Language Models
• Authors: Yukyung Lee, Soonwon Ka, Bokyung Son, Pilsung Kang, Jaewook Kang,
• Abstract summary: We propose Writing Path, a framework that uses explicit outlines to guide Large Language Models (LLMs) in generating user-aligned text. Our approach draws inspiration from structured writing planning and reasoning paths, focusing on capturing and reflecting user intentions throughout the writing process.
• Score: 8.920436030483872
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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