ArticleFindings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing2024
DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature.
Article in Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- From Promising Capabilities to Pervasive Bias: Assessing Large Language Models for Emergency Department Triage.Journal of healthcare informatics research · 2026Article
- A survey on LLM-as-a-judge.Innovation (Cambridge (Mass.)) · 2026Article
- Structured knowledge representation of the South China Sea: An LLM-based knowledge graph approach.PloS one · 2026Article
- A novel computational analysis integrating social determinants information from EHR and literature with Alzheimer's disease biological knowledge through large language models and knowledge graphs.Innovation in aging · 2025Article
- A Unified Framework for Alzheimer's Disease Knowledge Graphs: Architectures, Principles, and Clinical Translation.Brain sciences · 2025Review
- Knowledge-Driven Feature Selection and Engineering for Genotype Data with Large Language Models.ArXiv · 2025Article
- Knowledge-Driven Feature Selection and Engineering for Genotype Data with Large Language Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Enabling Few-Shot Alzheimer's Disease Diagnosis on Biomarker Data with Tabular LLMs.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2025Article
- Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge Graphs.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Large Language Models in Medicine: Applications, Challenges, and Future Directions.International journal of medical sciences · 2025Review
- FuncFetch: an LLM-assisted workflow enables mining thousands of enzyme-substrate interactions from published manuscripts.Bioinformatics (Oxford, England) · 2024Article
- Time Matters: Examine Temporal Effects on Biomedical Language Models.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
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Authors and funding
13 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.