Abstract
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.
Abstract (translated)
近年来，在自然语言处理（NLP）领域的大型语言模型（LLM）的进步已经取得了各种应用领域的 promising 表现。然而，长尾知识整合持续挑战仍然阻碍了 LLM 在专业领域的无缝采用。在这项工作中，我们引入了 DALK（动态协同增强LLM和KG），以解决这一局限，并证明其在研究阿尔茨海默病（AD）方面的能力，这是生物医学领域的一个专业子领域，也是全球健康优先事项。通过 LLM 和 KG 相互增强的协同框架，我们首先利用 LLM 构建一个从 AD 相关科学文献中不断演变的 AD 特定知识图（KG），然后我们利用一种新颖的自我感知知识检索方法，对 KG 进行粗到细的采样，以选择适当的知识来增强 LLM 的推理能力。实验结果表明，在构建的 AD 问题回答（ADQA）基准上进行实验时，DALK 的有效性得到了充分验证。此外，我们进行了一系列详细分析，可以提供有关增强 KG 和 LLM 的有益见解和指导。我们将发布代码和数据于该链接处。
URL
https://arxiv.org/abs/2405.04819
