Multi Hop Query

Multi-hop query answering aims to extract information requiring multiple reasoning steps from knowledge graphs or large text corpora. Current research focuses on improving the efficiency and accuracy of retrieval-augmented generation (RAG) methods, often employing iterative query refinement and LLM-based filtering to overcome limitations in handling complex queries. These advancements leverage techniques like embedding methods, logical message passing, and parallel algorithms to enhance performance, particularly for large-scale knowledge graphs and multi-modal data. The resulting improvements have significant implications for question answering systems, knowledge base completion, and other applications requiring complex reasoning over large datasets.