Multi Hop Question

Multi-hop question answering (MQA) focuses on developing systems capable of answering complex questions requiring reasoning across multiple pieces of information. Current research emphasizes improving the accuracy and efficiency of MQA systems, particularly by integrating knowledge graphs, employing iterative retrieval-augmented generation methods, and leveraging large language models (LLMs) enhanced with graph neural networks or chain-of-thought prompting. These advancements are crucial for improving the reliability and interpretability of AI systems, with significant implications for applications ranging from open-domain question answering to knowledge base querying and multimodal reasoning.