Interactive Question Answering

Interactive Question Answering (IQA) systems aim to create more natural and effective human-computer interactions by allowing users to refine their information requests through dialogue. Current research focuses on improving the accuracy and human-likeness of IQA responses, often employing large language models (LLMs) and techniques like retrieval-augmented generation to enhance response quality and alignment with user needs. This field is significant for advancing human-computer interaction and has practical applications in diverse areas, including education, healthcare (e.g., patient instruction), and general information access, where accurate and engaging conversational AI is crucial. Evaluating IQA systems effectively, including developing automated evaluation methods that correlate well with human judgment, is also a key area of ongoing investigation.