Integrating Planning into Single-Turn Long-Form Text Generation

Understand

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.