README.md

metadata

license: mit

LitVISTA: A Benchmark for Narrative Orchestration in Literary Text

Paper (arXiv:2601.06445)

📖 Overview

LitVISTA is a structurally annotated benchmark designed to evaluate the narrative orchestration capabilities of Large Language Models (LLMs). Unlike traditional benchmarks that focus on causal coherence or local semantics, LitVISTA maps stories into a high-dimensional representational framework called VISTA Space.

It focuses on how narrative events (Verbs+) serve distinct structural roles—Impulses, Resonances, and Pauses—to create the pacing, tension, and rhythm inherent in human-authored literature.

🗂️ Dataset Structure

The dataset is organized into train, val, and test splits (8:1:1 ratio). Each entry consists of two files:

• .txt file: The raw literary text (sourced from LitBank/Project Gutenberg).
• .ann file: The structural annotations in a tab-separated format.

Annotation Format

Each line in the .ann file follows this structure:

Index	Anchor Topology	Offset	Verb+	Narrative Dependency
0	Resonance	376,381	Smoke	49

1. Index: The unique identifier for the narrative anchor.
2. Anchor Topology: The functional role within VISTA Space:
   • Impulse (V_I): Plot drivers that form the narrative backbone and advance the plot to a new stage (τ → τ+1).
   • Resonance (V_R): Descriptive expansions that provide texture and context without advancing the plot stage (τ → τ+δ).
   • Pause (V_P): Vertical intensity markers that freeze narrative time to dive into psychology or micro-details (τ → τ).
3. Offset: Character-level start and end positions of the anchor in the source text.
4. Verb+: The textual anchor (canonical verbs or event-denoting nominals like "marriage").
5. Narrative Dependency: The ID of the parent node, defining the global structural backbone or recursive attachments.

📊 Key Statistics

Metric	Train	Val	Test
Avg. Length (Tokens)	10.2k	9.9k	10.7k
Predominant Role	Resonance	Resonance	Resonance
Complexity	High cross-dependency and long-range narrative relations.

🚀 Task: Narrative Structure Reconstruction

The core task is to reconstruct the narrative topology. In the Oracle Setting, models are provided with the text and the Verb+ anchors and must predict the Anchor Topology and Narrative Dependency (Head) for each.

📝 Citation

If you use this dataset, please cite our work:

@article{lu2026litvista,
  title={LitVISTA: A Benchmark for Narrative Orchestration in Literary Text},
  author={Lu, Mingzhe and Wang, Yiwen and Liu, Yanbing and You, Qi and Liu, Chong and others},
  journal={arXiv preprint arXiv:2601.06445},
  year={2026}
}