Context Dependent Question

Context-dependent question answering (CDQA) focuses on developing systems that accurately answer questions requiring understanding of both the question itself and its surrounding context. Current research emphasizes improving retrieval-augmented generation (RAG) methods, often employing techniques like determinantal point processes to select diverse and non-conflicting information sources, and exploring the use of large language models (LLMs) for question generation and answer evaluation. This field is crucial for advancing natural language understanding and has significant implications for applications such as education, healthcare, and information retrieval, particularly in scenarios with complex or ambiguous queries.