| --- |
| 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](https://huggingface.co/datasets/allenai/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](https://huggingface.co/collections/teagrjohnson/narratives-in-llm-pretraining-data). |
| |
| 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](https://arxiv.org/abs/2606.19468) |
| - **Collection:** [Narratives in LLM Pretraining Data](https://huggingface.co/collections/teagrjohnson/narratives-in-llm-pretraining-data) |
| - **Source corpus:** [allenai/dolma](https://huggingface.co/datasets/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](https://opendatacommons.org/licenses/by/1-0/), 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 |
| |
| ```bibtex |
| @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} |
| } |
| ``` |
| |
