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

2024-04-22

arXiv.org

AI Summary

Large language models (LLMs) often produce text with weak factual grounding, poor coherence, and misalignment with user intent. To address these issues, this paper proposes WritingPath—a novel outline-driven, structured writing framework that explicitly models human writing planning logic as a hierarchical outline and integrates it throughout the LLM generation process. Methodologically, WritingPath combines outline-aware generation, multi-stage prompt engineering, and human-in-the-loop evaluation for alignment optimization. It also introduces the first benchmark dataset for joint outline-text quality assessment, comprising real-world blog outline–text pairs. Experiments across multiple LLMs demonstrate significant improvements in factual accuracy, coherence, and user-intent alignment of generated text. Both automated metrics and expert writer evaluations confirm the framework’s effectiveness, establishing a new paradigm for controllable, high-fidelity LLM-based content generation.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Enhancing text generation quality
• Aligning text with user intentions
• Improving structured writing processes

Innovation

• Outline-guided text generation
• Structured writing planning
• Comprehensive evaluation framework