AI RESEARCH
StoryAlign: Evaluating and Training Reward Models for Story Generation
arXiv CS.AI
•
ArXi:2605.04831v1 Announce Type: cross Story generation aims to automatically produce coherent, structured, and engaging narratives. Although large language models (LLMs) have significantly advanced text generation, stories generated by LLMs still diverge from human-authored works regarding complex narrative structure and human-aligned preferences. A key reason is the absence of effective modeling of human story preferences, which are inherently subjective and under-explored. In this work, we systematically evaluate the modeling of human story preferences and.