Duplicate from anonymous12341952/Narrative-Infilling

Co-authored-by: anonymous2 <anonymous12341952@users.noreply.huggingface.co>

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README.md

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+---
+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.

infilling_dataset.csv

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