DALK : Dynamic Co-Augmentation of LLM s and KG to answer A lzheimer’s Disease Questions with Scientific Literature (2024)

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.

## introduction alzheimer's disease (ad) is a neurodegenerative disorder characterized by progressive declines in cognitive and functional status over a span of decades @xcite . however, current ad therapy developments are facing critical challenges due to the lack of knowledge and understanding of the underlying etiological mechanisms of the disease. although scientific literature and dedicated biomedical databases could supply rich sources of ad knowledge, manual review of relevant information is impossible due to the large volume. ## conclusion in this research, we begin by analyzing the main limitations of adopting the existing llms-based methods in ad-specific areas. to address these issues, we propose a novel approach in the merging of large language models and knowledge graphs in the context of alzheimer's disease. we provide an innovative dynamic co-augmentation framework for the refinement of large language models and knowledge graphs. initially, our approach extracts structural insights from the unstructured scientific literature, crafting a specialized knowledge graph for ad. subsequently, we employ a coarse-to-fine sampling technique coupled with a unique selfaware knowledge retrieval strategy to pinpoint relevant information from the knowledge graph. the extensive evaluation conducted in our constructed adqa benchmark shows the effectiveness of our method and provides further hints into the synergy of llms and kg in the context of ad.