xyingzhang/wikinewpages-2025-passages

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license: mit

Dataset Information

This dataset is organized in the following paper:
Self-Tuning: Instructing LLMs to Effectively Acquire New Knowledge through Self-Teaching
ACL Anthology Link

If you find this dataset useful, please cite the paper:

@inproceedings{zhang-etal-2025-self-tuning,
    title = "Self-Tuning: Instructing {LLM}s to Effectively Acquire New Knowledge through Self-Teaching",
    author = "Zhang, Xiaoying and
      Peng, Baolin and
      Tian, Ye and
      Zhou, Jingyan and
      Zhang, Yipeng and
      Mi, Haitao and
      Meng, Helen M.",
    editor = "Che, Wanxiang and
      Nabende, Joyce and
      Shutova, Ekaterina and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-acl.297/",
    pages = "5688--5724",
    ISBN = "979-8-89176-256-5",
    abstract = "Large language models (LLMs) often struggle to provide up-to-date information due to their one-time training and the constantly evolving nature of the world. To keep LLMs current, existing approaches typically involve continued pre-training on new documents. However, they frequently face difficulties in extracting stored knowledge. Motivated by the remarkable success of the Feynman Technique in efficient human learning, we introduce Self-Tuning, a learning framework aimed at improving an LLM's ability to effectively acquire new knowledge from unseen raw documents through self-teaching. Specifically, we develop a Self-Teaching strategy that augments the documents with a set of knowledge-intensive tasks created in a self-supervised manner, focusing on three crucial aspects: memorization, comprehension, and self-reflection. Additionally, we introduce three Wiki-Newpages-2023-QA datasets to facilitate an in-depth analysis of an LLM's knowledge acquisition ability concerning memorization, extraction, and reasoning. Extensive experimental results on various models, e.g., Llama2-7B reveal that Self-Tuning consistently exhibits superior performance across all knowledge acquisition tasks and excels in preserving previous knowledge."
}