A 7B writing model trained on plan-write-refine thinking data with multi-stage preference optimization matches or beats several larger models on long-form generation benchmarks.
Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation
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abstract
Story composition is a challenging problem for machines and even for humans. We present a neural narrative generation system that interacts with humans to generate stories. Our system has different levels of human interaction, which enables us to understand at what stage of story-writing human collaboration is most productive, both to improving story quality and human engagement in the writing process. We compare different varieties of interaction in story-writing, story-planning, and diversity controls under time constraints, and show that increased types of human collaboration at both planning and writing stages results in a 10-50% improvement in story quality as compared to less interactive baselines. We also show an accompanying increase in user engagement and satisfaction with stories as compared to our own less interactive systems and to previous turn-taking approaches to interaction. Finally, we find that humans tasked with collaboratively improving a particular characteristic of a story are in fact able to do so, which has implications for future uses of human-in-the-loop systems.
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SuperWriter: Reflection-Driven Long-Form Generation with Large Language Models
A 7B writing model trained on plan-write-refine thinking data with multi-stage preference optimization matches or beats several larger models on long-form generation benchmarks.