Abstract
Foreshadowing and payoff are ubiquitous narrative devices through which authors introduce commitments early in a story and resolve them through concrete, observable outcomes. However, despite advances in story generation, large language models (LLMs) frequently fail to bridge these long-range narrative dependencies, often leaving "Chekhov's guns" unfired even when the necessary context is present. Existing evaluations largely overlook this structural failure, focusing on surface-level coherence rather than the logical fulfillment of narrative setups. In this paper, we introduce Codified Foreshadowing-Payoff Generation (CFPG), a novel framework that reframes narrative quality through the lens of payoff realization. Recognizing that LLMs struggle to intuitively grasp the "triggering mechanism" of a foreshadowed event, CFPG transforms narrative continuity into a set of executable causal predicates. By mining and encoding Foreshadow-Trigger-Payoff triples from the BookSum corpus, we provide structured supervision that ensures foreshadowed commitments are not only mentioned but also temporally and logically fulfilled. Experiments demonstrate that CFPG significantly outperforms standard prompting baselines in payoff accuracy and narrative alignment. Our findings suggest that explicitly codifying narrative mechanics is essential for moving LLMs from surface-level fluency to genuine narrative competence.
Abstract (translated)
预示和回报是作者在故事早期引入承诺并在后续通过具体可观察结果解决这些承诺的普遍叙述手段。然而，尽管故事情节生成技术有所进步，大型语言模型（LLM）仍然经常无法跨越这些长跨度叙事依赖关系，即使存在必要背景时也常常未能触发“契诃夫之枪”，导致故事中的线索未被利用。现有的评估方法大多忽视这种结构性失败，关注的是表面连贯性而非叙述设定的逻辑实现。 在本文中，我们引入了编码预示-回报生成（Codified Foreshadowing-Payoff Generation, CFPG），这是一个通过实现回报来重新定义叙事质量的新框架。认识到LLM难以直观地理解预示事件的“触发机制”，CFPG将叙事连贯性转化为一组可执行的因果谓词。通过对BookSum语料库中的预示-触发-回报三元组进行挖掘和编码，我们提供了一种结构化监督方法，确保不仅提到预示承诺，而且在时间和逻辑上实现它们。实验结果表明，CFPG在回报准确性和叙事一致性方面显著优于标准提示基准模型。 我们的研究结果表明，明确编码叙述机制对于将LLM从表面流畅性提升到真正的叙事能力至关重要。
URL
https://arxiv.org/abs/2601.07033
