| --- |
| license: other |
| task_categories: |
| - text-generation |
| - fill-mask |
| tags: |
| - narrative infilling |
| - text infilling |
| - story understanding |
| - benchmark |
| - language model evaluation |
| language: |
| - en |
| pretty_name: NarrativeInfilling-Benchmark |
| size_categories: |
| - 1K<n<10K |
|
|
| rai:dataLimitations: |
| - The dataset is English only and does not support multilingual evaluation |
| - The benchmark spans four narrative domains; generalization to other genres or text types is not guaranteed |
| - Missing spans are contiguous sentence sequences of one to three sentences; non-contiguous or stylistic gaps are not covered |
| - Benchmark instances are drawn from publicly available corpora and may overlap with LLM pretraining data, though contamination analysis shows fewer than 0.15% of model responses exhibit near-verbatim reproduction |
|
|
| rai:dataBiases: |
| - Source corpora reflect the demographic and cultural biases present in Wikipedia, CNN/DailyMail, ROCStories, and SIND |
| - News articles (CNN/DailyMail) may contain temporal and political biases |
| - Commonsense stories (ROCStories) reflect everyday scenarios predominantly from Western cultural contexts |
| - Visual narratives (SIND) are grounded in Flickr image captions and may reflect photographer and platform biases |
|
|
| rai:personalSensitiveInformation: |
| - All documents are drawn from publicly available sources |
| - No individual personal identifiers are intentionally included |
| - Users should screen for personally identifiable information before redistribution or downstream use |
|
|
| rai:dataUseCases: |
| - Evaluating narrative infilling capabilities of instruction-tuned language models |
| - Benchmarking automatic and qualitative evaluation metrics for text generation |
| - Studying the effect of prompt design and reasoning guidance on narrative reconstruction |
| - Use cases for which validity has not been established include open-domain generation, multilingual infilling, and non-narrative text reconstruction |
|
|
| prov:wasDerivedFrom: |
| - name: Wikipedia |
| url: https://huggingface.co/datasets/wikimedia/wikipedia |
| - name: CNN/DailyMail |
| url: https://huggingface.co/datasets/abisee/cnn_dailymail |
| - name: ROCStories |
| url: https://cs.rochester.edu/nlp/rocstories/ |
| - name: SIND (Sequential Image Narrative Dataset) |
| url: https://visionandlanguage.net/VIST/dataset.html |
|
|
| prov:wasGeneratedBy: |
| - name: Data Collection |
| description: > |
| Narratives were sampled from four publicly available datasets spanning |
| encyclopedic text (Wikipedia), news articles (CNN/DailyMail), commonsense |
| stories (ROCStories), and visual narratives (SIND). Narratives were filtered |
| to ensure a minimum length suitable for span masking. |
| - name: Span Masking |
| description: > |
| One to three contiguous sentences were masked from each narrative to create |
| infilling instances. Blank positions (opening, middle, closing) were |
| systematically varied to ensure balanced coverage across narrative positions. |
| The masked span serves as the gold answer for evaluation. |
| - name: Quality Filtering |
| description: > |
| Instances were filtered to remove duplicates and narratives where masking |
| produced degenerate or trivially short contexts. |
| --- |
| |
| # Dataset Card for NarrativeInfilling Benchmark |
|
|
| ## Dataset Details |
|
|
| ### Dataset Description |
|
|
| The Narrative Infilling Benchmark is a large-scale evaluation dataset for |
| **narrative infilling** the task of generating a missing span within a |
| narrative while maintaining consistency with both the preceding and following |
| context. The benchmark spans four narrative domains and contains **9,142 instances** |
| with systematic variation in blank position and span length, enabling controlled |
| evaluation of language model infilling capabilities. |
|
|
| Each instance provides a narrative with one masked span (indicated by `____`) |
| and the corresponding gold answer. The benchmark is designed to evaluate |
| instruction-tuned LLMs under varying prompt specificity and reasoning guidance. |
|
|
| ## Uses |
|
|
| ### Direct Use |
| - **Narrative infilling evaluation:** Assess how well LLMs reconstruct missing |
| narrative spans across diverse genres and blank positions. |
| - **Prompt sensitivity analysis:** Study the effect of instruction specificity |
| and reasoning paradigms on Story Completion. |
| - **Metric evaluation:** Benchmark automatic metrics against qualitative |
| human judgments for narrative infilling tasks. |
|
|
| ### Out-of-Scope Use |
| - Not suitable for training language models directly without appropriate |
| data splits to prevent leakage. |
| - Not suitable for factual question answering or non-narrative text reconstruction. |
|
|
| ## Dataset Structure |
|
|
| **Format:** Single CSV file. |
|
|
| ### Fields |
|
|
| | Column | Description | |
| |--------|-------------| |
| | `dataset` | Source domain: `wikipedia`, `cnn_dailymail`, `roc`, `sind` | |
| | `ref_id` | Integer reference ID for the instance (0–999 per dataset) | |
| | `source_text` | Full narrative text | |
| | `problem` | The infilling problem presented to the model with masked span indicated by `____` | |
| | `gold_answer` | The original masked span serving as the reference answer | |
| | `n` | Number of sentences in the masked span (1, 2, or 3) | |
| | `unit_idx` | Position index of the masked span within the narrative | |
|
|
|
|
| ### Splits |
|
|
| The dataset is distributed as a single file. Users can split by `dataset` |
| column for domain-specific evaluation or by `unit_idx` for position-specific analysis. |
|
|
| ## Dataset Creation |
|
|
| ### Curation Rationale |
|
|
| No existing benchmark directly evaluates LLM narrative infilling across |
| multiple genres with controlled variation in blank position and span length. |
| This benchmark fills that gap by providing a standardized multi-domain |
| evaluation setting for narrative reconstruction. |
|
|
| ### Source Data |
|
|
| Instances were derived from four publicly available datasets: |
|
|
| | Domain | Source | Narrative Type | |
| |--------|--------|---------------| |
| | `wikipedia` | Wikipedia | Encyclopedic text | |
| | `cnn_dailymail` | CNN/DailyMail | News articles | |
| | `roc` | ROCStories | Commonsense stories | |
| | `sind` | SIND | Sequential Image Narrative Dataset | |
|
|
| ### Annotation Process |
|
|
| No additional human annotation was performed during dataset construction. |
| The gold answers are the original masked sentences from the source texts. |
| Human evaluation of model outputs was conducted separately using a |
| five-dimensional qualitative rubric (Fluency, Context Faithfulness, |
| Bidirectional Coherence, Narrative Consistency, Informativeness) as |
| described in the accompanying paper. |
|
|
| ## Bias, Risks, and Limitations |
|
|
| - **Domain shift:** Performance varies substantially across domains; |
| models strong on CNN/DailyMail may not generalize to SIND. |
| - **Pretraining overlap:** Source corpora are publicly available and |
| may appear in LLM pretraining data. Contamination analysis in the |
| accompanying paper shows a very small fraction of responses exhibit |
| near-verbatim reproduction. |
| - **Contiguous spans only:** Real-world narrative gaps may involve |
| non-contiguous or stylistic missing content not covered by this benchmark. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{narrativeinfilling2025, |
| title = {Evaluating Narrative Infilling in Large Language Models}, |
| year = {2025}, |
| } |
| ``` |
|
|
| ## More Information |
|
|
| This release supports the reproducibility of the results reported in the |
| accompanying paper. The benchmark data, evaluation code, and model |
| outputs are publicly available in the accompanying repository. |
