Open Domain Conversational Question Answering

Open-domain conversational question answering (ODConvQA) focuses on building systems that can engage in multi-turn conversations and answer questions using information retrieved from vast, unstructured text corpora. Current research emphasizes improving the accuracy and efficiency of these systems, addressing challenges like hallucination, weak reasoning, and ineffective information retrieval. This is achieved through advancements in retrieval methods (e.g., dense passage retrieval, phrase retrieval), reader models (often leveraging large language models), and techniques like conversational context modeling and query rewriting. ODConvQA's progress holds significant implications for developing more natural and informative human-computer interaction, particularly in applications requiring access to and reasoning over large knowledge bases.