Retrieval-Augmented Generation for LLM Systems

Collaborative research on grounding large language model outputs in retrieved evidence, spanning two complementary directions.

The hybrid retrieval approach combines sentence-window and parent-child retrieval with a re-ranking stage, improving both the relevance of retrieved passages and the faithfulness of generated answers to their source material. The knowledge-graph extension (KG-RAG) targets multi-hop question answering by decomposing questions and using in-context learning with chain-of-thought prompting, so that multi-step retrieval produces explicit and inspectable reasoning chains instead of opaque single-shot answers. The method requires no training, which keeps it adaptable across different knowledge graphs with minimal human effort, and improves accuracy on multi-hop questions in the MetaQA benchmark.

Both projects treat retrieval as infrastructure for inspectable reasoning, addressing when a language model should be trusted and when structured evidence should constrain it.