UltraEditBench

UltraEditBench is the largest publicly available dataset to date for the task of model editing.

This dataset was introduced in the paper:

ULTRAEDIT: Training-, Subject-, and Memory-Free Lifelong Editing in Large Language Models

---

📦 Dataset Overview

These components enable evaluation along three metrics:

Metric	Description
Efficacy	Whether the model correctly reflects the updated fact.
Generalization	Whether the edit applies to semantically similar questions.
Specificity	Whether unrelated knowledge remains unaffected.

Each sample in UltraEditBench includes three core instances (each a question–answer pair):

Component	Description	Count
Editing Instance	A factual question-answer pair involving the target entity, used to test Efficacy.	2,008,326
Equivalent Instance	A paraphrased version of the editing instance, used to test Generalization.	2,008,326
Unrelated Instance	An unrelated question-answer pair, used to test Specificity.	2,008,326

🔑 Key Descriptions

Each sample in UltraEditBench includes three full instances (question–answer pairs) and associated metadata:

Key	Description
case_id	Unique identifier for the sample (e.g., "00001").
prompt	The question part of the Editing Instance — a factual question targeting a specific knowledge update.
ans	The answer part of the Editing Instance — the desired output after the model is edited.
subject	The entity mentioned in the editing question. Provided for compatibility with subject-centric methods.
rephrase_prompt	The question part of the Equivalent Instance — a paraphrased version of the prompt.
loc	The question part of the Unrelated Instance — factually unrelated to the editing fact.
loc_ans	The answer part of the Unrelated Instance — should remain unchanged after editing.

---

💡 Citation

If you use this dataset, please cite:

@article{gu2025ultraedit,
  title={UltraEdit: Training-, Subject-, and Memory-Free Lifelong Editing in Large Language Models},
  author={Gu, Xiaojie and Chen, Guangxu and Li, Jungang and Gu, Jia-Chen and Hu, Xuming and Zhang, Kai},
  journal={arXiv preprint arXiv:2505.14679},
  year={2025}
}

📨 Contact

• Email: peettherapynoys@gmail.com
• GitHub Issues: github.com/XiaojieGu/UltraEdit