Open Domain Question

Open-domain question answering (ODQA) focuses on building systems that can answer factual questions using vast, unstructured knowledge sources, aiming to replicate human-like comprehension and reasoning. Current research emphasizes improving the robustness and efficiency of ODQA systems, particularly by addressing challenges like ambiguity, multilingualism, and the need for complex reasoning across diverse data types (text, tables, knowledge graphs). This involves developing advanced retrieval methods, often incorporating large language models (LLMs) within retriever-reader pipelines or employing techniques like chain-of-thought prompting, and refining evaluation metrics to move beyond simple lexical matching. The advancements in ODQA have significant implications for various applications, including conversational AI, information retrieval, and knowledge-based systems.