Knowledge Graph-Driven Retrieval-Augmented Generation: Integrating Deepseek-R1 with Weaviate for Advanced Chatbot Applications
This addresses reliability issues in biomedical chatbots for healthcare professionals, though it is an incremental improvement combining existing techniques in a domain-specific context.
The paper tackles the problem of LLMs generating unverified outputs in biomedical applications by proposing a retrieval-augmented generation framework that integrates structured knowledge graphs with LLMs for age-related macular degeneration (AMD). The result is a system that notably decreases hallucinations, enhances factual precision, and improves response clarity in biomedical chatbot applications.
Large language models (LLMs) have significantly advanced the field of natural language generation. However, they frequently generate unverified outputs, which compromises their reliability in critical applications. In this study, we propose an innovative framework that combines structured biomedical knowledge with LLMs through a retrieval-augmented generation technique. Our system develops a thorough knowledge graph by identifying and refining causal relationships and named entities from medical abstracts related to age-related macular degeneration (AMD). Using a vector-based retrieval process and a locally deployed language model, our framework produces responses that are both contextually relevant and verifiable, with direct references to clinical evidence. Experimental results show that this method notably decreases hallucinations, enhances factual precision, and improves the clarity of generated responses, providing a robust solution for advanced biomedical chatbot applications.