Datasets:

nworats
/

MemFactory

pretty_name: MemFactory
license: cc-by-sa-4.0
language:
  - en
task_categories:
  - question-answering
tags:
  - long-context
  - evaluation
  - question-answering
  - multi-hop
  - hotpotqa
  - synthetic
configs:
  - config_name: eval_50
    data_files:
      - split: train
        path: eval_50.json
  - config_name: eval_100
    data_files:
      - split: train
        path: eval_100.json
  - config_name: converted_hotpotqa_2000
    data_files:
      - split: train
        path: converted_hotpotqa_2000.json
  - config_name: eval_fwe_16384
    data_files:
      - split: train
        path: eval_fwe_16384.json

MemFactory

Overview

This repository provides a lightweight, derivative release of data used in MemFactory.

To evaluate the effectiveness of MemFactory, we reuse and adapt data from the upstream dataset:

BytedTsinghua-SIA/hotpotqa

This repository includes four JSON files:

eval_50.json
eval_100.json
eval_fwe_16384.json
converted_hotpotqa_2000.json

Data Sources

The three evaluation files are directly derived from the upstream HotpotQA-based release.
The training file converted_hotpotqa_2000.json is a locally adapted version of the upstream training data, modified for MemFactory experiments.

For full dataset context, please refer to the upstream release:

https://huggingface.co/datasets/BytedTsinghua-SIA/hotpotqa

Limitations

This is a derivative redistribution, not the original dataset.
The data may inherit:
- annotation noise
- biases
- structural limitations
  from the upstream sources.
eval_fwe_16384.json follows a different schema from the QA-style files.
For full documentation and broader coverage, users should consult the upstream dataset.

License

This repository is released under CC BY-SA 4.0.

Reason:

The data is derived from the upstream HotpotQA-based dataset, which uses the same license.
converted_hotpotqa_2000.json is an adapted derivative and must preserve share-alike terms.

If you use or redistribute this repository:

Please retain attribution to the upstream source
Preserve the same license

Loading with 🤗 datasets

from datasets import load_dataset

eval_50 = load_dataset("nworats/MemFactory", "eval_50", split="train")
eval_100 = load_dataset("nworats/MemFactory", "eval_100", split="train")
train_converted = load_dataset("nworats/MemFactory", "converted_hotpotqa_2000", split="train")
eval_fwe_16384 = load_dataset("nworats/MemFactory", "eval_fwe_16384", split="train")

Citation

If you use this dataset, please cite:

MemFactory (this work)

(Placeholder – replace with your paper when available)

@article{memfactory2025,
  title={MemFactory: [Your Subtitle Here]},
  author={Your Name et al.},
  journal={arXiv preprint arXiv:XXXX.XXXXX},
  year={2025}
}

Upstream MemAgent work

@article{yu2025memagent,
  title={MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent},
  author={Yu, Hongli and others},
  journal={arXiv preprint arXiv:2507.02259},
  year={2025}
}