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This work proposes Select-ANd-Extract (SANE), a simple yet effective plugin for RAG that performs blueprint-guided query-time evidence extraction, which allows the generator LM to use only compact and structured key information so that it can perform better reasoning.
Large language models (LLMs) have transformed AI, yet they remain fundamentally limited by hallucination, unverifiable reasoning, and shallow evidence grounding. We argue that structure mining-rooted in decades of KDD research on taxonomy induction, ontology design, entity typing, and knowledge graph construction-is th...
This tutorial presents a unified vision in which structuring serves as the enabling foundation for three pillars of next-generation LLM systems, highlighting how the cooperative interplay between classical KDD techniques and modern LLMs-where KDD defines structural schemas and quality constraints while LLMs execute fle...
This work proposes EnSI-RAG (Entity-Structure-Indexed Retrieval-Augmented Generation), a framework that constructs a query-independent, entity-centered index that separates evidence localization from answer synthesis while preserving traceable source evidence.
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