Extractive question answering is one of the most versatile NLP techniques and is widely used in production to extract information from documents
Questions help us better understand the world around us by filling in our information gaps. Similarly, we can use extractive question answering (QA) — a computational method for information extraction — to gain a better understanding of our textual data.
This method uses questions to extract information from documents. Because it is infinitely scalable, it can pinpoint the exact location of a piece of information, even in huge documents and data sets.
Extractive QA for information retrieval has been used successfully in production for many years. It is much safer for highly sensitive use cases than newer techniques like retrieval augmented generation (RAG) because, unlike generative AI, extractive QA is bound to be faithful to the underlying dataset.
This method uses relatively small encoder models that are available in many of the world's languages. They're open source, free of charge, and adhere to the highest standards of data security. They can be stacked on top of a vector search retrieval module to form a modular system that can be easily updated and evaluated.
With deepset Cloud, our model-agnostic LLM platform, even small teams can build production-grade systems for information extraction — powered by question answering — in very little time.
Connect the pipeline to your data to extract fact-checked information from your own documents within fractions of a second, and without any risk of hallucinations.
Customer service support, question answering on top of your company’s knowledge base, and using QA to create new metadata are just some of the applications of question answering systems.
Textual data is growing by the second, and it’s becoming increasingly important to be able to make sense of that data through a natural language interface.
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