StableKE

This is the repository for our paper Stable Knowledge Editing in Large Language Models.

This paper introduces StableKE, a novel method for knowledge editing in large language models (LLMs), focusing on stability across multiple dimensions.

StableKE incorporates Semantic Paraphrase Enhancement and Contextual Description Enrichment strategies to maintain edited, multi-hop, and unrelated knowledge stability, as well as general model capabilities.

We introduce KEBench, a novel benchmark specifically designed for assessing knowledge editing methods. This benchmark rigorously evaluates the stability of edited knowledge, its ripple effects across multi-hop knowledge integration, the preservation of unrelated knowledge.

Datasets

Overview

The KEBench datasets are included in dataset/. There are three files:

• edited_knowledge.json: Edited Knowledge Stability reflects the performance of one-hop knowledge editing, focusing on the consistency and accuracy of edited knowledge.
• multi_hop_knowledge.json: Multi-hop Knowledge Stability evaluates how well the edited knowledge integrates with existing knowledge across multiple steps.
• unrelated_knowledge.json: Unrelated Knowledge Stability ensures that unrelated knowledge remains unchanged despite the editing process.

Data format

Edited Knowledge

The dataset is saved as a list of dicts, each of which represents a data instance. An example in edited_knowledge.json is shown below.

{
  "first_question": "What is the country of citizenship of Paul Douglas?",
  "paraphrased_question": "What is Paul Douglas' nationality?",
  "old_prompt": "Paul Douglas is a citizen of United States of America.",
  "old_first_answer": "United States of America",
  "old_first_answer_aliases": ["US",…],
  "new_prompt": "Paul Douglas is a citizen of France.",
  "new_first_answer": "France",
  "new_first_answer_aliases": ["FR",…],
}

• first_question: Generate using templates based on fact triples $t_1 = (s_1,r_1,o_1)$
• papaphrased_question: Based on the first question, the question has been paraphrased using ChatGPT while preserving semantic equivalence.
• old_prompt: Correct answer sentence before editing.
• old_first_answer and old_first_answer_aliases: Correct answer and its alias before editing.
• new_prompt: Correct answer sentence after editing.
• new_first_answer and new_first_answer_aliases: Correct answer and its alias after editing.

Multi Hop Knowledge

The dataset is saved as a list of dicts, each of which represents a data instance. An example in multi_hop_knowledge.json is shown below.

{
  "two_hop_question": "Who is the head of state of the country whose citizen is Paul Douglas?",
  "old_multi_hop_answer": "Joe Biden",
  "old_multi_hop_answer_aliases": ["Biden",…]
  "new_multi_hop_answer": "Emmanuel Macron",
  "new_multi_hop_answer_aliases": ["Macron",…],
}

• two_hop_question: A combination problem generated based on the first and second fact triples $t_1 = (s_1,r_1,o_1) & t_2 = (s_2, r_2, o_2)$and following the template.
• old_multi_hop_answer and old_multi_hop_answer_aliases: Correct answer and its alias before editing.
• new_multi_hop_answer and new_multi_hop_answer_aliases: Correct answer and its alias after editing.

Unrelated Knowledge

The dataset is saved as a list of dicts, each of which represents a data instance. An example in unrelated_knowledge.json is shown below.

{
  "old_second_question": "What is the name of the current head of state in United States of America?",
  "new_second_question": "What is the name of the current head of state in France?",
  "old_second_answer": "Joe Biden",
  "old_second_answer_aliases": ["Biden",…]
  "new_second_answer": "Emmanuel Macron",
  "new_second_answer_aliases": ["Macron",…],
}

• old_second_question: The question related to answer entities $o_1$ before editing.
• new_second_question: The question related to answer entities $o_1^*$ after editing.
• old_second_answer and old_second_answer_aliases: The answer to old_second_question and its alias.
• new_second_answer and new_second_answer_aliases: The answer to new_second_question and its alias.

Evaluation

We include evaluation setups that we use in our paper.

Edited facts (single-hop)

For a single-hop problem, you can test the results output by the model by running the following code.

python stableke/code/evaluate/edited_knowledge_check.py

Multi-hop questions (including CoT)

For multi-hop problems that include COT, you can test the model results by running the following code.

python stableke/code/evaluate/multi_hop_knowledge_check.py

Unrelated facts (single-hop)

For unrelated questions, you can test the model results by running the following code.

python stableke/code/evaluate/unrelated_knowledge_check.py

Acknowledgments

We would like to express our gratitude to the projects, research and development personnel associated with a portion of the MQuAKE dataset utilized in this project.

• MQuAKE Github
• MQuAKE Paper

Citation

If you use our code or dataset in your research, please cite our work:

@misc{wei2024stable,
      title={Stable Knowledge Editing in Large Language Models}, 
      author={Zihao Wei and Liang Pang and Hanxing Ding and Jingcheng Deng and Huawei Shen and Xueqi Cheng},
      year={2024},
      eprint={2402.13048},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}