Recent advancements in large language models (LLMs) have achieved promising
performances across various applications. Nonetheless, the ongoing challenge of
integrating long-tail knowledge continues to impede the seamless adoption of
LLMs in specialized domains. In this work, we introduce DALK, a.k.a. Dynamic
Co-Augmentation of LLMs and KG, to address this limitation and demonstrate its
ability on studying Alzheimer's Disease (AD), a specialized sub-field in
biomedicine and a global health priority. With a synergized framework of LLM
and KG mutually enhancing each other, we first leverage LLM to construct an
evolving AD-specific knowledge graph (KG) sourced from AD-related scientific
literature, and then we utilize a coarse-to-fine sampling method with a novel
self-aware knowledge retrieval approach to select appropriate knowledge from
the KG to augment LLM inference capabilities. The experimental results,
conducted on our constructed AD question answering (ADQA) benchmark, underscore
the efficacy of DALK. Additionally, we perform a series of detailed analyses
that can offer valuable insights and guidelines for the emerging topic of
mutually enhancing KG and LLM. We will release the code and data at
this https URL

利用 DALK，一种 LLM 和 KG 相互增强的动态协同方法，我们将大规模语言模型应用于阿尔茨海默病领域，构建了一个与 AD 相关的知识图谱，并利用知识图谱增强 LLM 的推理能力，实验证明了其有效性。