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arxiv: 2510.02025 · v4 · submitted 2025-10-02 · 💻 cs.CL

Style over Story: Measuring LLM Narrative Preferences via Structured Selection

Donghoon Jung , Jiwoo Choi , Songeun Chae , Seohyon Jung This is my paper

Pith reviewed 2026-05-18 10:43 UTC · model grok-4.3

classification 💻 cs.CL
keywords LLM narrative preferencesstyle vs contentconstraint selectionnarratologycreative AImodel evaluationstory generation biases
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The pith

Large language models prioritize stylistic elements over narrative content such as events, characters, and settings.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces an experiment where LLMs select from a library of 200 narratology-grounded constraints to measure their narrative preferences. Tests on six models under basic, quality-focused, and creativity-focused instructions reveal a consistent choice of style constraints over content ones like events, characters, and settings. This finding matters because it uncovers stable latent biases that could shape how models perform in creative writing and storytelling applications. Content preferences vary more across models and instructions, suggesting they are more sensitive to training differences and prompt details.

Core claim

Using selections from a library of 200 narratology-grounded constraints, the study finds that LLMs consistently prioritize style over content elements like Event, Character, and Setting. Style preferences remain stable across different models and instruction types, while content elements exhibit cross-model divergence and sensitivity to instructions. These patterns indicate that LLMs carry latent narrative preferences relevant to their use in creative domains.

What carries the argument

Constraint-selection task based on a library of 200 narratology-grounded constraints that measures latent narrative preferences by frequency of selection under varied instructions.

If this is right

  • LLMs possess latent narrative preferences that should inform evaluation and deployment in creative domains.
  • Style preferences stay consistent regardless of model or instruction type.
  • Content preferences for events, characters, and settings diverge across models and shift with instructions.
  • Narrative evaluations of LLMs must account for these stable style biases to avoid skewed creative outputs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The selection method could be extended to audit preferences in related generative tasks such as dialogue or poetry.
  • Training data likely over-represents stylistic features compared to plot or character coherence.
  • This approach offers a lightweight way to test model behaviors without generating complete stories.

Load-bearing premise

The library of 200 constraints provides a balanced and unbiased representation of narrative elements so that selection frequency measures true preferences rather than wording effects or prompt artifacts.

What would settle it

If models selected constraints uniformly across style and content categories or if rewording the constraints eliminated the style preference, the claim of consistent style prioritization would not hold.

read the original abstract

We introduce a constraint-selection-based experiment design for measuring narrative preferences of Large Language Models (LLMs). This design offers an interpretable lens on LLMs' narrative selection behavior. We developed a library of 200 narratology-grounded constraints and prompted selections from six LLMs under three different instruction types: basic, quality-focused, and creativity-focused. Findings demonstrate that models consistently prioritize Style over narrative content elements like Event, Character, and Setting. Style preferences remain stable across models and instruction types, whereas content elements show cross-model divergence and instructional sensitivity. These results suggest that LLMs have latent narrative preferences, which should inform how the NLP community evaluates and deploys models in creative domains.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces a constraint-selection experiment to measure LLMs' narrative preferences. A library of 200 narratology-grounded constraints is used to prompt six LLMs under three instruction types (basic, quality-focused, creativity-focused). The central finding is that models consistently prioritize Style constraints over content elements (Event, Character, Setting), with Style preferences stable across models and instructions while content elements vary.

Significance. If the measurement is robust, the work offers an interpretable, selection-based lens on latent narrative biases in LLMs that could guide evaluation protocols for creative generation tasks. The reported stability of Style preference across models and instructions is a potentially useful empirical observation for the field.

major comments (2)
  1. [§3] §3 (Constraint Library): The manuscript states that the 200 constraints are 'narratology-grounded' but provides no distribution counts per category (Style vs. Event vs. Character vs. Setting), no inter-rater validation of category assignments, and no controls for wording length or salience. Because selection frequency is the sole dependent measure, unequal category sizes or more salient Style phrasings would artifactually inflate the Style-over-content result; this directly undermines the claim that higher Style selections reflect genuine prioritization.
  2. [§4.2] §4.2 (Results): The abstract and results claim 'consistent' prioritization and 'stable' Style preferences, yet the text does not report per-condition sample sizes, statistical tests (e.g., chi-square or mixed-effects models), error bars, or prompt-sensitivity controls. Without these, it is impossible to assess whether the Style dominance is robust or driven by the specific phrasing of the three instruction types.
minor comments (2)
  1. [Table 1] Table 1: The column headers for instruction types are abbreviated without a legend; expand or add a footnote for clarity.
  2. [§2] §2 (Related Work): The discussion of narratology sources is brief; adding one or two canonical references (e.g., to Propp or Genette) would help readers situate the constraint taxonomy.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment point by point below, indicating the revisions we will make to strengthen the manuscript.

read point-by-point responses

  1. Referee: [§3] §3 (Constraint Library): The manuscript states that the 200 constraints are 'narratology-grounded' but provides no distribution counts per category (Style vs. Event vs. Character vs. Setting), no inter-rater validation of category assignments, and no controls for wording length or salience. Because selection frequency is the sole dependent measure, unequal category sizes or more salient Style phrasings would artifactually inflate the Style-over-content result; this directly undermines the claim that higher Style selections reflect genuine prioritization.

    Authors: We agree that these details are necessary for a fully transparent interpretation of the results. In the revised manuscript we will add (1) a table reporting the exact distribution of the 200 constraints across the four categories, (2) average word lengths and lexical complexity measures per category to demonstrate comparability, and (3) an expanded description of the constraint-generation process, which was grounded in canonical narratology sources and iteratively refined by the author team. While a formal multi-rater validation study was not conducted, we will explicitly note this and discuss its implications. These additions directly address the risk of artifactual inflation. revision: yes

  2. Referee: [§4.2] §4.2 (Results): The abstract and results claim 'consistent' prioritization and 'stable' Style preferences, yet the text does not report per-condition sample sizes, statistical tests (e.g., chi-square or mixed-effects models), error bars, or prompt-sensitivity controls. Without these, it is impossible to assess whether the Style dominance is robust or driven by the specific phrasing of the three instruction types.

    Authors: We accept that the current presentation of results is insufficiently rigorous. In the revision we will report per-condition sample sizes, apply chi-square tests (or appropriate mixed-effects models) to selection frequencies, include error bars on all figures, and add a prompt-sensitivity analysis that varies instruction phrasing while holding other factors constant. These changes will provide quantitative support for the claims of consistency and stability. revision: yes

Circularity Check

0 steps flagged

No significant circularity: empirical selection frequencies are self-contained

full rationale

The paper's central result consists of observed selection frequencies from LLMs choosing among a fixed library of 200 constraints under controlled instructions. No equations, fitted parameters, or self-citations reduce these frequencies to the inputs by construction. The library is presented as an external narratology-grounded resource whose balance is an empirical premise, not a definitional loop. The derivation therefore remains independent of the measured outcomes and qualifies as a direct experimental measurement against external model behavior.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Central claim depends on the assumption that constraint selections validly reveal latent preferences and that the narratology library is comprehensive and non-overlapping.

axioms (1)
  • domain assumption The 200 narratology-grounded constraints accurately partition narrative elements into distinct categories (Style, Event, Character, Setting) without significant overlap or selection bias from wording.
    Invoked when interpreting selection frequencies as evidence of stable preferences.

pith-pipeline@v0.9.0 · 5645 in / 1197 out tokens · 33761 ms · 2026-05-18T10:43:43.037250+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Narrative Landscape: Mapping Narrative Dispositions Across LLMs

    cs.CL 2026-05 unverdicted novelty 7.0

    The study maps LLM narrative selection behaviors onto a 'Narrative Landscape' using consistency (Jaccard) and diversity (inverse Simpson) metrics, revealing a rigidity-exploration spectrum across models and instructio...