Long-Form Generation › Summary

Long-form generation extends language model capabilities to document-scale tasks. The core challenges are context window limits, topic drift, and factual consistency. The main techniques to address these challenges are:

• Outline-based generation: Separate planning from writing. Generate an outline first, then use it as a structural anchor during section-by-section content generation.
• Hierarchical generation: Decompose documents into nested levels (document plan, section plans, paragraph plans, prose) to give each generation step clear, narrow scope.
• Entity tracking: Maintain a registry of key entities and their attributes, injecting it into each section's prompt to prevent inconsistencies.
• Transition generation: Generate explicit bridging sentences between sections to improve local coherence at section boundaries.
• Coherence scoring: Use entity grid models or neural coherence models to automatically assess whether a generated document holds together.
• Coverage evaluation: Check that the generated document addresses the required topics by comparing keyword presence against a predefined topic list.
• Consistency checking: Extract numerical and factual claims from generated text and flag potential contradictions for human review.

Combining these techniques allows generation of documents that maintain coherent structure, consistent facts, and broad coverage across thousands of tokens. Long-form generation is fundamentally a planning problem: models that write without a plan drift, contradict themselves, and lose their way. Models that plan first, then write, can produce documents that serve their intended purpose.