metadata
license: mit
task_categories:
- summarization
language:
- en
tags:
- diffusion
- flow-matching
- language-modeling
- elf
This repository contains the pre-tokenized XSum dataset used in the paper ELF: Embedded Language Flows.
The dataset is tokenized using the T5 tokenizer and is prepared for use with ELF, a class of continuous-time Flow Matching models that operate in the continuous embedding space of a frozen T5 encoder.
- GitHub Repository: https://github.com/lillian039/ELF
- Paper: ELF: Embedded Language Flows
Dataset Summary
For the summarization task (XSum), the data is structured for conditional generation:
condition_input_ids: Tokenized source text (the article).input_ids: Tokenized target text (the summary).
The ELF model prepends the condition IDs to the input IDs and applies specific attention masks during training and inference.
Sample Usage
You can load this dataset directly using the datasets library:
from datasets import load_dataset
# Load the training split
dataset = load_dataset("embedded-language-flows/xsum_train_t5")
# Example of an item in the dataset
print(dataset["train"][0])
Citation
@article{elf2026,
title={ELF: Embedded Language Flows},
author={Hu, Keya and Qiu, Linlu and Lu, Yiyang and Zhao, Hanhong and Li, Tianhong and Kim, Yoon and Andreas, Jacob and He, Kaiming},
journal={arXiv preprint arXiv:2605.10938},
year={2026}
}