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 support systems.
From the blog
How to Train ChatGPT With Your Own Website Data
Training ChatGPT with your own data can provide the model with a better understanding of your unique context, allowing for more accurate and relevant responses.
Herman Schutte
Founder
How AI Chatbots Can Save You 100s Of Hours In Customer Support
Dive into the transformative power of AI chatbots in customer support. Learn how businesses can save significant time and enhance customer satisfaction, with a look at tools like SiteSpeakAI.
Herman Schutte
Founder