MLaKE

This is the repository for our paper MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models.

The paper introduces MLaKE, a novel and extensive benchmark for assessing knowledge editing in large language models (LLMs) across multiple languages, including English, Chinese, Japanese, French, and German.

The MLaKE benchmark comprises 5,360 single-hop and 4,072 multi-hop questions generated from Wikipedia fact chains. It aims to evaluate the adaptability of LLMs to edits in their knowledge base and their ability to generalize this edited knowledge across different languages.

Experimental results from the study indicate that while existing knowledge editing methods are effective in English, their performance significantly drops in other languages, highlighting a gap in current methodologies when it comes to multilingual knowledge editing.

The findings also show that models have a higher generalization capability within the same language family compared to languages from different families, suggesting a linguistic bias in current knowledge editing approaches.

Datasets

Overview

The MLaKE datasets are included in dataset/. There are four folders:

• single_hop/qa/: This folder contains five files, each corresponding to single-hop free-form QA files in five different languages.
• single_hop/mcqa/: This folder contains five files, each corresponding to single-hop MCQA files in five different languages.
• multi_hop/qa/: This folder contains ten files, each corresponding to pre-edit and post-edit free-form QA files in five different languages.
• multi_hop/mcqa/: This folder contains ten files, each corresponding to pre-edit and post-edit MCQA files in five different languages.

Data format

Single Hop Free Form QA

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

{
  "question": "Where did Carl Sagan work during his career?",
  "o_prompt": "Carl Sagan worked at Cornell University during his career.",
  "o_answer": "Cornell University",
  "o_answer_aliases": [
      "Cornell",
      "CUI",
      "cornell.edu",
      "CU",
      "Cornell Univ."
  ],
  "e_prompt": "Carl Sagan worked at BBC during his career.",
  "e_answer": "BBC",
  "e_answer_aliases": []
}

• question: Generate using ChatGPT based on single-hop fact chains.
• o_prompt: Correct answer sentence before editing.
• o_answer and o_answer_aliases: Correct answer and its alias before editing.
• e_prompt: Correct answer sentence after editing.
• e_answer and e_answer_aliases: Correct answer and its alias after editing.

Single Hop Multiple Choice QA

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

{
  "question": "Where did Carl Sagan work during his career?",
  "options": [
      "Carl Sagan worked at Harvard University during his career.",
      "Carl Sagan worked at BBC during his career.",
      "Carl Sagan worked at NASA's Jet Propulsion Laboratory during his career.",
      "Carl Sagan worked at Cornell University during his career."
  ],
  "o_prompt": "Carl Sagan worked at Cornell University during his career.",
  "o_answer": "D",
  "e_prompt": "Carl Sagan worked at BBC during his career.",
  "e_answer": "B"
}

• question: Generate using ChatGPT based on single-hop fact chains.
• options: Four options for multiple choice.
• o_prompt: Correct answer sentence before editing.
• o_answer: Correct option before editing.
• e_prompt: Correct answer sentence after editing.
• e_answer: Correct option after editing.

Multi Hop Free Form QA

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

{
    "question": "Where is the primary headquarters of the institution where Carl Sagan was employed located?",
    "o_answer": "Ithaca",
    "o_answer_aliases": [
        "Ithaca (town), New York",
        "Ithaca, New York"
    ]
}

• question: A combination problem generated based on the multi-hop fact chains and following the ChatGPT.
• o_prompt: Correct answer sentence before editing.
• o_answer and o_answer_aliases: Correct answer and its alias before editing.

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

{
    "question": "By whom was the institution where Carl Sagan worked established?",
    "e_prompt": "Carl Sagan worked at BBC during his career.",
    "e_answer": "John Reith, 1st Baron Reith",
    "e_answer_aliases": [],
}

• question: A combination problem generated based on the multi-hop fact chains and following the ChatGPT.
• e_prompt: Correct answer sentence after editing.
• e_answer and e_answer_aliases: Correct answer and its alias after editing.

Multi Hop Multiple Choice QA

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

{
    "question": "Where is the primary headquarters of the institution where Carl Sagan was employed located?",
    "options": [
        "The primary headquarters of the institution where Carl Sagan was employed is located in Los Angeles.",
        "The primary headquarters of the institution where Carl Sagan was employed is located in New York City.",
        "The primary headquarters of the institution where Carl Sagan was employed is located in Chicago.",
        "The primary headquarters of the institution where Carl Sagan was employed is located in Ithaca."
    ],
    "e_answer": "D"
},

• question: A combination problem generated based on the multi-hop fact chains and following the ChatGPT.
• o_prompt: Correct answer sentence before editing.
• options: Four options for multiple choice.
• o_answer: Correct option before editing.

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

{
    "question": "In which country is the institution where Carl Sagan was employed located?",
    "e_prompt": "Carl Sagan worked at BBC during his career.",
    "options": [
        "The institution where Carl Sagan was employed is located in Canada.",
        "The institution where Carl Sagan was employed is located in the United States.",
        "The institution where Carl Sagan was employed is located in Australia.",
        "The institution where Carl Sagan was employed is located in the United Kingdom."
    ],
    "e_answer": "D"
}

• question: A combination problem generated based on the multi-hop fact chains and following the ChatGPT.
• e_prompt: Correct answer sentence after editing.
• options: Four options for multiple choice.
• e_answer: Correct option after editing.

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{wei2024mlake,
      title={MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models}, 
      author={Zihao Wei and Jingcheng Deng and Liang Pang and Hanxing Ding and Huawei Shen and Xueqi Cheng},
      year={2024},
      eprint={2404.04990},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}