Question Answering (QA) systems automatically answer natural language questions, either by extracting answers from provided documents (extractive QA) or generating answers from learned knowledge (generative QA). ' answered by highlighting '1889' in the context. The model predicts start and end positions of the answer span within the document.
Generative QA produces free-form answers without requiring supporting documents, relying on knowledge encoded in model parameters during pretraining. Large language models excel at this but can hallucinate plausible-sounding but incorrect answers.
Open-domain QA combines retrieval and reading: first find relevant documents from a large corpus, then extract or generate answers from retrieved content. This RAG approach grounds answers in evidence. Multi-hop QA requires reasoning across multiple pieces of information. Conversational QA handles follow-up questions that reference previous context.
Table QA answers questions about structured data. Visual QA answers questions about images. Evaluation typically uses exact match and F1 scores against reference answers.