Multi Hop Question Generation

Multi-hop question generation (MQG) focuses on creating complex questions requiring reasoning across multiple information sources, a crucial step towards more sophisticated AI interaction. Current research emphasizes improving the quality and diversity of synthetic training data, developing end-to-end models that avoid intermediate question labeling, and incorporating structured rationales or question-answering modules to enhance both the generation process and the interpretability of the resulting questions. These advancements aim to produce more challenging and realistic questions for evaluating and training question-answering systems, ultimately contributing to more robust and explainable AI systems.