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        <title>Fiction Flows: A Replication and Reinterpretation of Narrative Sequentiality</title>
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    <abstract>Narrative flow emerges from the interplay between memory and expectation, shaping how stories are both produced and understood. To operationalize this construct, Sap et al. (2022) propose sequentiality, a language-model–based measure of sentence-level predictability, and report that imagined stories flow better than recalled ones. We conduct a large-scale replication across multiple language models, examine how modeling choices shape the original findings, and test generalization beyond crowdworker data using passages from published fiction and narrative non-fiction. Although the original contrast replicates under their initial formulation, it diminishes substantially under alternative specifications, suggesting that it reflects properties of the measurement setup rather than a stable feature of narrative flow. By contrast, fiction does appear to exhibit a robust sequentiality advantage over reality-bound genres under a minimal context-only formulation. However, mixed-effects analyses indicate that this advantage is not reducible to standard coherence measures, underscoring the need for further theoretical and empirical grounding of narrative flow.</abstract>
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%0 Conference Proceedings
%T Fiction Flows: A Replication and Reinterpretation of Narrative Sequentiality
%A Piper, Andrew
%A Hamilton, Sil
%A Zhou, Haiqi
%A Pianzola, Federico
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F piper-etal-2026-fiction
%X Narrative flow emerges from the interplay between memory and expectation, shaping how stories are both produced and understood. To operationalize this construct, Sap et al. (2022) propose sequentiality, a language-model–based measure of sentence-level predictability, and report that imagined stories flow better than recalled ones. We conduct a large-scale replication across multiple language models, examine how modeling choices shape the original findings, and test generalization beyond crowdworker data using passages from published fiction and narrative non-fiction. Although the original contrast replicates under their initial formulation, it diminishes substantially under alternative specifications, suggesting that it reflects properties of the measurement setup rather than a stable feature of narrative flow. By contrast, fiction does appear to exhibit a robust sequentiality advantage over reality-bound genres under a minimal context-only formulation. However, mixed-effects analyses indicate that this advantage is not reducible to standard coherence measures, underscoring the need for further theoretical and empirical grounding of narrative flow.
%U https://aclanthology.org/2026.acl-long.1576/
%P 34158-34174

Markdown (Informal)

[Fiction Flows: A Replication and Reinterpretation of Narrative Sequentiality](https://aclanthology.org/2026.acl-long.1576/) (Piper et al., ACL 2026)

• Fiction Flows: A Replication and Reinterpretation of Narrative Sequentiality (Piper et al., ACL 2026)

ACL

• Andrew Piper, Sil Hamilton, Haiqi Zhou, and Federico Pianzola. 2026. Fiction Flows: A Replication and Reinterpretation of Narrative Sequentiality. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 34158–34174, San Diego, California, United States. Association for Computational Linguistics.