Codified Foreshadowing-Payoff Text Generation
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.
6965e456fc8c4ecc02c7fa36
Longfei Yun
6965e456fc8c4ecc02c7fa37
Kun Zhou
6965e456fc8c4ecc02c7fa38
Yupeng Hou
claimed_verified
2026-01-13T08:23:31.019Z
64a62c2f500beb50968e5c9c
https://cdn-avatars.huggingface.co/v1/production/uploads/64a62c2f500beb50968e5c9c/ydpojlCWaAOF68OIaEwM7.jpeg
Yupeng Hou
hyp1231
user
hyp1231
6965e456fc8c4ecc02c7fa39
Letian Peng
6965e456fc8c4ecc02c7fa3a
Jingbo Shang
Codified Foreshadowing-Payoff Text Generation
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.
Large language models struggle with maintaining long-range narrative dependencies, but a new framework called CFPG addresses this by structuring narrative continuity through executable causal predicates to ensure proper fulfillment of foreshadowed events.
Codified Foreshadowing-Payoff Generation narrative quality payoff realization causal predicates BookSum corpus foreshadow-Trigger-Payoff triples narrative continuity surface-level coherence narrative competence