@inproceedings{zhang-etal-2024-knowledge-editing,
    title = "Knowledge Editing for Large Language Models",
    author = "Zhang, Ningyu  and
      Yao, Yunzhi  and
      Deng, Shumin",
    editor = "Klinger, Roman  and
      Okazaki, Naozaki  and
      Calzolari, Nicoletta  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024): Tutorial Summaries",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-tutorials.6",
    pages = "33--41",
    abstract = "Even with their impressive abilities, Large Language Models (LLMs) such as ChatGPT are not immune to issues of factual or logically consistent. Concretely, the key concern is how to seamlessly update those LLMs to correct mistakes without resorting to an exhaustive retraining or continuous training procedure, both of which can demand significant computational resources and time. Thus, the capability to edit LLMs offers an efficient solution to alter a model{'}s behavior, notably within a distinct area of interest, without negatively impacting its performance on other tasks. Through this tutorial, we strive to acquaint interested NLP researchers with recent and emerging techniques for editing LLMs. Specifically, we aim to present a systematic and current overview of cutting-edge methods, supplemented with practical tools, and unveil new research opportunities for our audiences. All the valuable resources can be accessed at https://github.com/zjunlp/KnowledgeEditingPapers.",
}

Even with their impressive abilities, Large Language Models (LLMs) such as ChatGPT are not immune to issues of factual or logically consistent. Concretely, the key concern is how to seamlessly update those LLMs to correct mistakes without resorting to an exhaustive retraining or continuous training procedure, both of which can demand significant computational resources and time. Thus, the capability to edit LLMs offers an efficient solution to alter a model{'}s behavior, notably within a distinct area of interest, without negatively impacting its performance on other tasks. Through this tutorial, we strive to acquaint interested NLP researchers with recent and emerging techniques for editing LLMs. Specifically, we aim to present a systematic and current overview of cutting-edge methods, supplemented with practical tools, and unveil new research opportunities for our audiences. All the valuable resources can be accessed at https://github.com/zjunlp/KnowledgeEditingPapers.
Zhang, Ningyu Yao, Yunzhi Deng, Shumin
Knowledge Editing for Large Language Models