Shengyu Chen is a Researcher in the Data Science and System Security Department at NEC Laboratories America, based in Princeton, NJ. He earned his Master’s degree in Computer Science from Indiana University Bloomington and completed his Ph.D. in Computer Science at the University of Pittsburgh. At NEC, Dr. Chen develops advanced AI methods for analyzing complex spatial-temporal and time-series data. His work integrates generative modeling, foundation models, large language models, and knowledge-guided machine learning techniques to address challenges in cybersecurity, system monitoring, and intelligent automation. He designs algorithms that extract actionable insights from massive, heterogeneous datasets, enabling more accurate detection, prediction, and decision-making in critical IT and OT environments. His research bridges cutting-edge machine learning techniques with practical applications that enhance resilience, efficiency, and security across diverse domains.
Most retrieval-augmented generation systems stop at documents, ignoring the relational databases that power finance, healthcare, and research. Our researchers built a rule-driven framework that learns which source to query for each question, delivering better answers at lower computational cost.
Large Language Models (LLMs) have shown remarkable performance on general Question Answering (QA), yet they often struggle in domain-specific scenarios where accurate and up-to-date information is required. Retrieval-Augmented Generation (RAG) addresses this limitation by enriching LLMs with external knowledge, but existing systems primarily rely on unstructured documents, while largely overlooking relational databases, which provide precise, timely, and efficiently queryable factual information, serving as indispensable infrastructure in domains such as finance, healthcare, and scientific research. Motivated by this gap, we conduct a systematic analysis that reveals three central observations: (i) databases and documents offer complementary strengths across queries, (ii) naively combining both sources introduces noise and cost without consistent accuracy gains, and (iii) selecting the most suitable source for each query is crucial to balance effectiveness and efficiency. We further observe that query types show consistent regularities in their alignment with retrieval paths, suggesting that routing decisions can be effectively guided by systematic rules that capture these patterns. Building on these insights, we propose a rule-driven routing framework designed specifically for hybrid-source RAG. A routing agent scores candidate augmentation paths based on explicit rules and selects the most suitable one; a rule-making expert agent refines the rules using QA feedback to produce more comprehensive and reliable decision criteria; and a path-level meta-cache reuses past routing decisions for semantically similar queries to reduce latency and cost. Experiments on three QA datasets demonstrate that our framework consistently outperforms static strategies and learned routing baselines, achieving higher accuracy while maintaining moderate computational cost.
