apeleg/LKF-baseline-challenger-train

LKF baseline data for the Challenger disaster topic: training splits

This dataset holds the training splits of the LKF baseline comparison data used in the paper "Forget Narrowly, Retain Broadly: Unlearning as an Asymmetric Generalization Problem". It adapts the Challenger disaster topic of the LKF (Lesser-Known Facts) dataset, introduced in "Unlearning That Lasts" (Singh et al., 2025), to the format of our unlearning pipeline.

It is not part of the SUITE benchmark. It exists to reproduce the paper's dataset comparison experiment, in which the same Challenger disaster facts are unlearned by training on LKF data instead of SUITE data.

The three baseline repositories

Repository	Contents
LKF-baseline-challenger-train (this repository)	training splits
LKF-baseline-challenger-forget-eval	forget evaluation questions with rephrasings
LKF-baseline-challenger-retain-eval	semantic retain evaluation questions with rephrasings

Splits and columns

Split	Rows	Columns
forget_train	400	question, answer, label
retain_train_semantic	375	question, answer, label

Usage

The paper's code consumes these datasets through the challenger_baseline topic config:

TOPIC=challenger_baseline bash scripts/suite_unlearn.sh

This experiment runs on Llama only and uses its own hyperparameter defaults (10 epochs, random forget-retain pairing).

Related datasets

The SUITE benchmark itself: apeleg/SUITE and apeleg/SUITE-rephrasings. The full taxonomy and results are on the project page.

Citation

@article{peleg2026forget,
    title   = {Forget Narrowly, Retain Broadly: Unlearning as an Asymmetric Generalization Problem},
    author  = {Peleg, Amit and Singh, Naman Deep and Pearl, Naama and Mohapatra, Bibhabasu and Hein, Matthias},
    journal = {arXiv preprint arXiv:2607.09236},
    year    = {2026}
}

The original LKF dataset:

@article{singh2025unlearning,
    title   = {Unlearning That Lasts: Utility-Preserving, Robust, and Almost Irreversible Forgetting in LLMs},
    author  = {Singh, Naman Deep and M{\"u}ller, Maximilian and Croce, Francesco and Hein, Matthias},
    journal = {arXiv preprint arXiv:2509.02820},
    year    = {2025}
}