HotPotQA Dataset

HotPotQA is a benchmark dataset for multi-hop question answering, challenging models to synthesize information from multiple sources to answer complex questions. Current research focuses on improving model performance using techniques like retrieval-augmented generation (RAG), incorporating structured data like lists, and developing more robust evaluation metrics beyond simple accuracy. These advancements aim to enhance the factual accuracy and explainability of question-answering systems, impacting applications ranging from customer service chatbots to educational tools. The dataset's design and the ongoing research contribute significantly to the broader field of natural language understanding.