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README.md

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-Retrieval Augmented Generation (RAG) has become a popular application for large language models. It is preferable that successful RAG systems provide accurate answers that are supported by being grounded in a passage without any hallucinations. While considerable work is required for building a full RAG pipeline, being able to benchmark performance is also necessary. We present CLAP NQ, a benchmark Long-form Question Answering dataset for the full RAG pipeline. CLAP NQ includes long answers with grounded gold passages from Natural Questions (NQ) and a corpus to perform either retrieval, generation, or the full RAG pipeline. The CLAP NQ answers are concise, 3x smaller than the full passage, and cohesive, with multiple pieces of the passage that are not contiguous.
+We present CLAP NQ, a benchmark Long-form Question Answering dataset for the full RAG pipeline. CLAP NQ includes long answers with grounded gold passages from Natural Questions (NQ) and a corpus to perform either retrieval, generation, or the full RAG pipeline. The CLAP NQ answers are concise, 3x smaller than the full passage, and cohesive, with multiple pieces of the passage that are not contiguous.
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+This is the annotated data for the generation portion of the RAG pipeline.
 
 For more information, visit: https://github.com/primeqa/clapnq