Question Answering

Question answering asks how systems can find, interpret, and combine information to answer questions, from short factual queries to multi-hop reasoning over documents, conversations, images, video, and structured data. Common approaches include retrieval-augmented generation, fine-tuning, knowledge graphs, tool-using agents, and symbolic methods; evaluations span reading comprehension, open-domain and long-document QA, conversational QA, and specialized domains. Current work also focuses on multilingual and multimodal settings, personalization, and recognizing when to clarify or abstain rather than guess. A central tension is that better retrieval or reasoning does not always produce more reliable answers: systems must still handle missing or conflicting evidence, avoid hallucinations, and balance accuracy, coverage, latency, and cost.