README.md

metadata

dataset_info:
  - config_name: agency_setting_narradolma_labels
    features:
      - name: id
        dtype: string
      - name: pred_focalization
        dtype: float64
      - name: pred_emotion
        dtype: float64
      - name: pred_cognition
        dtype: float64
      - name: pred_change_of_state
        dtype: float64
      - name: pred_conflict
        dtype: float64
      - name: pred_concreteness
        dtype: float64
      - name: pred_temporal_grounding
        dtype: float64
      - name: pred_spatial_grounding
        dtype: float64
      - name: pred_sensory
        dtype: float64
    splits:
      - name: train
        num_bytes: 9887439742
        num_examples: 13292499
    download_size: 4842804886
    dataset_size: 9887439742
  - config_name: event_relation_narradolma_labels
    features:
      - name: id
        dtype: string
      - name: pair_idx
        dtype: int64
      - name: span1
        dtype: string
      - name: span2
        dtype: string
      - name: pred_temporal_sequential
        dtype: int64
      - name: pred_causal
        dtype: int64
      - name: temporal_logit
        dtype: float64
      - name: causal_logit
        dtype: float64
    splits:
      - name: train
        num_bytes: 2070667684
        num_examples: 12212303
    download_size: 648459411
    dataset_size: 2070667684
configs:
  - config_name: agency_setting_narradolma_labels
    data_files:
      - split: train
        path: agency_setting_narradolma_labels/train-*
  - config_name: event_relation_narradolma_labels
    data_files:
      - split: train
        path: event_relation_narradolma_labels/train-*
license: odc-by
language:
  - en
pretty_name: NarraDolma
tags:
  - Narrative
  - Classification
size_categories:
  - 1M<n<10M

NarraDolma

NarraDolma is a large-scale narrative characterization of Dolma, the 3-trillion-token open pretraining corpus. It contains ~3M passages drawn from ~785K unique documents across all 12 Dolma sub-corpora, each labeled with a fine-grained narrative feature vector produced by NarraBert.

It is the corpus behind the paper Characterizing Narrative Content in Web-scale LLM Pretraining Data and is intended as a resource for studying how narrative qualities are distributed across pretraining data.

• Paper: arXiv:2606.19468
• Collection: Narratives in LLM Pretraining Data
• Source corpus: allenai/dolma

What's in the dataset

Each row is a 3-sentence passage with its Dolma provenance and a 12-feature narrative vector: the 11 NarraBert dimensions plus a derived event-density feature.

Group	Feature	Range	Description
Agency	focalization, emotion, cognition, change_of_state, conflict	1–5	Centrality of each agency feature
Setting	concreteness, temporal_grounding, spatial_grounding, sensory	1–5	How fully the storyworld is realized
Event relations	temporal_sequencing, causal_density	0–1	Fraction of adjacent event pairs that are temporally / causally related
Derived	event_density	≥0	Event triggers per token

Provenance fields: dolma_id (for rehydration), source (Dolma sub-corpus), and topic (WebOrganizer topic, Common Crawl passages only).

How it was built

1. Extraction — ~17M 3-sentence passages sampled from ~5M unique Dolma v1.7 documents, allocated proportionally across sources.
2. Narrative scoring — a DeBERTa binary narrative classifier scores each passage; used to focus sampling on coherent narrative content.
3. Topic classification — Common Crawl passages receive a WebOrganizer topic label (24 topics).
4. Stratified sampling — passages drawn preserving inter-source proportions, with 25% drawn without narrative filtering to keep non-narrative text represented. NarraBert then labels every passage. For analysis, passage vectors can be aggregated to the document level (averaging across a document's passages), yielding one narrative vector per ~785K documents.

Intended use & important caveats

• Relative, not absolute. NarraDolma deliberately over-represents narrative content, so these features describe relative concentration within the sample, not prevalence in raw Dolma. Statements like "X% of source Y is high-interiority" reflect the enriched sample only.
• Event features are noisier. The temporal sequencing and causal density features come from a model that underperforms its LLM teacher on event relations; treat them as higher-noise than the agency and setting features.
• Aggregate to the document level for analysis where possible — passages within a document are not independent.

Rehydration

Each row carries the Dolma unique ID so the original document text can be rejoined from the source corpus.

License & ethical considerations

Released under ODC-By, inherited from Dolma. Dolma is web-scraped and contains toxic, explicit, and personal content. Because NarraDolma pairs passage text with narrative feature labels, it could be misused to retrieve or amplify sensitive personal narratives. It is released for research and auditing; please do not use it to target, extract, or amplify sensitive personal disclosures.

Citation

@misc{johnson2026narrative,
  title         = {Characterizing Narrative Content in Web-scale LLM Pretraining Data},
  author        = {Johnson, Teagan and Ash, Elliott and Piper, Andrew and Antoniak, Maria},
  year          = {2026},
  eprint        = {2606.19468},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2606.19468}
}