| --- |
| language: |
| - en |
| pretty_name: LingGen |
| task_categories: |
| - text-generation |
| size_categories: |
| - 1M<n<10M |
| --- |
| |
| # LingGen |
|
|
| Official dataset release for the paper **LingGen: Scalable Multi-Attribute Linguistic Control via Power-Law Masking**. |
|
|
| This dataset contains the processed training and test data used for the LingGen experiments, with precomputed linguistic control vectors for each example. |
|
|
| ## Dataset Summary |
|
|
| - `train`: 6,810,672 examples |
| - `test`: 2,000 examples |
| - `ling`: 40 released control attributes used by the public codebase |
| - `ling_all`: full 276-dimensional linguistic feature vector |
|
|
| Each example includes: |
|
|
| - `sentence`: target text |
| - `source`: source dataset identifier |
| - `ling`: released 40-attribute control vector |
| - `ling_all`: full feature vector before selecting the released subset |
|
|
| ## Source Data |
|
|
| The processed examples are derived from public datasets used in the paper, including: |
|
|
| - C4 |
| - SMF |
| - QQP |
| - ANLI |
| - MRPC |
| - STS-B |
| - RTE |
|
|
| This release redistributes processed text and derived linguistic features for research use. Users should consult the original source datasets for their respective licenses and usage terms. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("mohdelgaar/LingGen") |
| ``` |
|
|
| To use the dataset directly with the released code repository, save it to disk first: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("mohdelgaar/LingGen") |
| dataset.save_to_disk("data/ling_sentences") |
| ``` |
|
|
| Code repository: https://github.com/CLU-UML/LingGen |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{elgaar2026linggen, |
| title={LingGen: Scalable Multi-Attribute Linguistic Control via Power-Law Masking}, |
| author={Mohamed Elgaar and Hadi Amiri}, |
| year={2026} |
| } |
| ``` |
|
|
