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## Dataset Summary
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**RetrievalQA** is a short-form open-domain question answering (QA) dataset consisting of 1,271 questions covering new world and long-tail knowledge.
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We ensure the knowledge necessary to answer the questions is absent from LLMs. Therefore, LLMs must truthfully decide whether to retrieve to be able to answer the questions correctly.
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RetrievalQA enables us to evaluate the effectiveness of **adaptive retrieval-augmented generation (RAG)** approaches, an aspect predominantly overlooked
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in prior studies and recent RAG evaluation systems, which focus only on task performance, the relevance of retrieval context or the faithfulness of answers.
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## Dataset Summary
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**RetrievalQA** is a short-form open-domain question answering (QA) dataset consisting of 1,271 questions covering new world and long-tail knowledge.
|
16 |
+
We ensure the knowledge necessary to answer the questions is absent from most LLMs. Therefore, LLMs must truthfully decide whether to retrieve to be able to answer the questions correctly.
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RetrievalQA enables us to evaluate the effectiveness of **adaptive retrieval-augmented generation (RAG)** approaches, an aspect predominantly overlooked
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19 |
in prior studies and recent RAG evaluation systems, which focus only on task performance, the relevance of retrieval context or the faithfulness of answers.
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