A Theoretical Framework for Evaluating Narrative Surprise in Large Language Models
Bibliographic record
Abstract
Narrative surprise is a core element of storytelling for engaging audiences, and yet it remains underexplored in the context of large language models (LLMs) and narrative generation.While surprise arises from events that deviate from expectations while maintaining retrospective coherence, current computational approaches lack comprehensive frameworks to evaluate this phenomenon.This paper presents a novel framework for assessing narrative surprise, drawing on psychological theories of narrative comprehension and surprise intensity.We operationalize six criteria-initiatoriness, immutability violation, predictability, post-dictability, importance, and valence-to measure narrative surprise in story endings.Our study evaluates 120 story endings, generated by both human authors and LLMs, across 30 mystery narratives.Through a ranked-choice voting methodology, we identify significant correlations between reader preferences and four of the six criteria.Results underscore the continuing advantage of humanauthored endings in achieving compelling narrative surprise, while also revealing significant progress in LLM-generated narratives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".