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Also known as: QA System
Computational task that answers natural-language questions using a given context or knowledge source.
DESCRIPTION
Question Answering (QA) systems return precise answers to user questions expressed in natural language. They commonly operate over documents, knowledge bases, or retrieved passages and may involve extractive or generative methods. Open-domain QA uses retrieval over large corpora, while closed-book QA relies on parametric model knowledge. Modern systems often combine dense retrieval with large language models as readers. QA underpins search assistants, customer support bots, and enterprise knowledge discovery. Key concerns include accuracy, latency, and grounding answers in verifiable sources. Evaluation typically uses exact match and F1 metrics on benchmark datasets. Responsible QA design considers bias, hallucinations, and provenance, especially in high-stakes contexts.
ALTERNATIVE DEFINITIONS
Hugging Face
Question answering is the task of answering questions given a context or a collection of documents.
Highlights the role of accompanying context or documents for QA.
IBM
Question answering uses natural language processing to automatically answer questions posed by humans in natural language.
Emphasizes NLP techniques and human-language queries.
Stanford (Introduction to Information Retrieval)
Question answering (QA) is the task of automatically answering a question posed in natural language.
Places QA within classic IR, stressing automation and natural language.
RELATED TERMS
Machine Reading Comprehension Open-domain Question Answering Closed-book Question Answering Retrieval-Augmented Generation Semantic Search Passage Ranking Named Entity Recognition Text Summarization Chatbot
COMPETING TERMS
RELATIONS
KNOWLEDGE AREAS
TARGET AUDIENCE
AI Engineer Data Scientist MLOps Engineer AI Product Manager Data Engineer
EXTERNAL SOURCES
- Wikipedia - Question answering
- Hugging Face - Overview of QA task and models
- Stanford - IR textbook chapter on QA
- Google Cloud - RAG overview for grounded QA
- Microsoft - Azure Language Service QA overview
TOP QUESTIONS
What is Question Answering in AI?
Question Answering is an NLP task where systems return precise answers to natural-language questions using documents, databases, or model knowledge. It contrasts with keyword search by providing direct answers.
Category: Definition
Why is Question Answering still relevant?
QA powers enterprise knowledge search, support bots, and assistants. With RAG and LLMs, QA delivers grounded, conversational answers, improving productivity and user experience.
Category: Relevancy
What are the main types of Question Answering?
Common types include extractive QA, generative QA, open-domain QA, closed-book QA, and knowledge-base QA. Each differs in how context is obtained and how answers are produced.
Category: Taxonomy
How does retrieval-augmented generation improve Question Answering?
RAG retrieves relevant passages and feeds them to a generator, grounding responses in sources. This reduces hallucinations and enables up-to-date, domain-specific answers.
Category: Technique
What metrics evaluate Question Answering systems?
Standard metrics include exact match and F1 for extractive QA, alongside human evaluations for factuality, faithfulness, and helpfulness in generative QA.
Category: Testing
What data is required for Question Answering?
You need question-answer pairs and supporting contexts for supervised training, or a searchable corpus for retrieval. High-quality, labeled datasets improve robustness.
Category: Requirements
What risks exist in deploying Question Answering?
Risks include hallucinations, privacy leakage, biased answers, and legal exposure from incorrect advice. Mitigations include grounding, red-teaming, and human-in-the-loop review.
Category: Risks
How does Question Answering differ from Information Retrieval?
IR returns documents or passages, while QA returns direct answers. Modern QA often layers a reader over IR to extract or generate precise responses.
Category: Comparison
Where should I start learning about Question Answering?
Begin with the Stanford IR book’s QA chapter, Hugging Face task pages, and hands-on RAG tutorials from major cloud providers.
Category: Learning
What is a common misunderstanding about Question Answering?
Many assume larger models alone ensure correctness. In practice, careful retrieval, prompt design, and evaluation are crucial for factual, reliable answers.
Category: Misunderstanding