Despite their success at many natural language processing (NLP) tasks, large
language models (LLMs) still struggle to effectively leverage knowledge for
knowledge-intensive tasks, manifesting limitations such as generating
incomplete, non-factual, or illogical answers. These limitations stem from
inadequate knowledge awareness of LLMs during vanilla fine-tuning. To address
these problems, we propose a knowledge-aware fine-tuning (KnowTuning) method to
explicitly and implicitly improve the knowledge awareness of LLMs. We devise an
explicit knowledge-aware generation stage to train LLMs to explicitly identify
knowledge triples in answers. We also propose an implicit knowledge-aware
comparison stage to train LLMs to implicitly distinguish between reliable and
unreliable knowledge, in three aspects: completeness, factuality, and
logicality. Extensive experiments on both generic and medical question
answering (QA) datasets confirm the effectiveness of KnowTuning, through
automatic and human evaluations, across various sizes of LLMs. Finally, we
demonstrate that the improvements of KnowTuning generalize to unseen QA
datasets.

使用知识感知微调（KnowTuning）方法解决大型语言模型在自然语言处理任务中的知识感知不足问题，并在通用和医疗问答数据集上进行了广泛的实验证明了其有效性，并展示了其在未知问答数据集上的泛化能力。