Self-Tuning: Instructing LLMs to Effectively Acquire New Knowledge through Self-Teaching
<|reference_start|>Self-Tuning: Instructing LLMs to Effectively Acquire New Knowledge through Self-Teaching: 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 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. In addition, 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 Llama2 family models reveal that Self-Tuning consistently exhibits superior performance across all knowledge acquisition tasks and excels in preserving previous knowledge.<|reference_end|>
@article{zhang2024self-tuning:,
    title={Self-Tuning: Instructing LLMs to Effectively Acquire New Knowledge
  through Self-Teaching},
    author={Xiaoying Zhang, Baolin Peng, Ye Tian, Jingyan Zhou, Yipeng Zhang,
  Haitao Mi, Helen Meng},
    journal={arXiv preprint arXiv:2406.06326},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06326},
    primaryClass={cs.CL}
}