Narrative Landscape: Mapping Narrative Dispositions Across LLMs
Donghoon Jung, Jiwoo Choi, Songeun Chae, Seohyon Jung

TL;DR
This paper introduces a quantitative framework to profile LLM dispositions using a structured narrative task, revealing a rigidity-exploration spectrum and how instruction types influence model behavior.
Contribution
It presents a novel PCA-based visualization called Narrative Landscape to compare model selection profiles and uncovers how instructions alter model dispositions.
Findings
Models exhibit a rigidity-exploration spectrum.
Instruction types shift selection space geometry.
Scalar metrics can mask qualitative differences.
Abstract
This study proposes a quantitative framework for profiling LLM dispositions as stable, model-specific regularities in output under repeated, controlled elicitation. Using a structured narrative constraint-selection task administered across six frontier models and three instruction types, we operationalize disposition through two dimensions: "consistency", measured as cross-replication selection overlap via Jaccard similarity, and "diversity", measured as dispersion across options via the inverse Simpson index. We further introduce Narrative Landscape, a PCA-based visualization that maps each model's selection profile into a shared space for direct comparison. Results reveal a clear rigidity-exploration spectrum across model families and show that instruction types shift the geometry of selection spaces even when scalar metrics appear similar, indicating that comparable scores can mask…
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