# Narrative Landscape: Mapping Narrative Dispositions Across LLMs Source: [Narrative Landscape: Mapping Narrative Dispositions Across LLMs]() Collection index: [llms.txt]() Data updated: 2026-05-13T05:00:05.563Z Authors: Donghoon Jung, Jiwoo Choi, Songeun Chae, Seohyon Jung Published: 2026-05-09T07:06:57.000Z ## 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 qualitatively distinct selection topologies. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Narrative Generation on Narrative Constraints Dataset](): Narrative Generation; Narrative Constraints Dataset; Jaccard Similarity: not recorded. ## Original sources and implementation [Original paper]() [PDF]()