Navigating the Path of Writing: Outline-guided Text Generation with Large Language Models
This work addresses the problem of improving text generation for content creation platforms, offering a domain-specific solution that is incremental in nature.
The authors tackled the challenge of generating high-quality, user-aligned text with Large Language Models by proposing WritingPath, a framework that uses explicit outlines to guide text generation, and demonstrated that it significantly enhances text quality in evaluations with various LLMs and professional writers.
Large Language Models (LLMs) have impacted the writing process, enhancing productivity by collaborating with humans in content creation platforms. However, generating high-quality, user-aligned text to satisfy real-world content creation needs remains challenging. We propose WritingPath, a framework that uses explicit outlines to guide LLMs in generating goal-oriented, high-quality text. Our approach draws inspiration from structured writing planning and reasoning paths, focusing on reflecting user intentions throughout the writing process. To validate our approach in real-world scenarios, 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 various LLMs demonstrate that the WritingPath approach significantly enhances text quality according to evaluations by both LLMs and professional writers.