用单一辅助任务让大模型先规划再写长文，提升内容质量。
Integrating Planning into Single-Turn Long-Form Text Generation
- 通过生成大纲等中间步骤训练模型规划能力
- 在两个数据集上实现ROUGE-Lsum提升2.5%，人类评估胜率3.60
- 适合需要结构化长文本生成的研究与写作场景
生成高质量、深度的文本文档（如学术论文、新闻、维基条目和书籍）仍是大语言模型面临的重大挑战。本文提出通过规划来生成长篇内容。为实现目标，我们设计了一个辅助任务，让模型在生成最终文本前学会规划、推理与结构组织。其核心创新在于仅需一个辅助任务，无需多轮提示或迭代规划。为解决中间步骤训练数据稀缺问题，我们利用大模型从已有完整文章中合成大纲、关键信息和摘要等数据。实验在来自不同领域的两个数据集——科学新闻数据集SciNews以及KILT-Wiki和FreshWiki维基数据集上进行，结果表明，经过该辅助任务微调的模型生成的文档质量更高。相比基线，ROUGE-Lsum提升2.5%，人类一对一评估中胜率比达3.60，尤其在结构组织、相关性和可验证性方面表现显著更优。
原文摘要 · Abstract (English)
Generating high-quality, in-depth textual documents, such as academic papers, news articles, Wikipedia entries, and books, remains a significant challenge for Large Language Models (LLMs). In this paper, we propose to use planning to generate long form content. To achieve our goal, we generate intermediate steps via an auxiliary task that teaches the LLM to plan, reason and structure before generating the final text. Our main novelty lies in a single auxiliary task that does not require multiple rounds of prompting or planning. To overcome the scarcity of training data for these intermediate steps, we leverage LLMs to generate synthetic intermediate writing data such as outlines, key information and summaries from existing full articles. Our experiments demonstrate on two datasets from different domains, namely the scientific news dataset SciNews and Wikipedia datasets in KILT-Wiki and FreshWiki, that LLMs fine-tuned with the auxiliary task generate higher quality documents. We observed +2.5% improvement in ROUGE-Lsum, and a strong 3.60 overall win/loss ratio via human SxS evaluation, with clear wins in organization, relevance, and verifiability.
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