What is Open-Domain Question Answering?
A task where AI systems answer questions using information retrieved from a wide range of unstructured data sources.
More about Open-Domain Question Answering:
Open-Domain Question Answering (QA) involves building systems that can answer questions by retrieving and processing information from unstructured sources such as articles, documents, or databases. These systems often integrate retrieval-based models with generative AI for accurate and coherent answers.
Techniques like dense retrieval and semantic search are commonly used to fetch relevant content, which is then processed by a language model for response generation.
Frequently Asked Questions
What makes open-domain QA different from traditional QA?
Open-domain QA requires retrieving and reasoning over a vast range of unstructured data, unlike traditional QA, which focuses on fixed datasets.
What are common applications of open-domain QA?
Applications include search engines, virtual assistants, and customer service systems.
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