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: Identifying when a provided document does not contain the answer is a critical real-world skill that models still struggle with.

The data represents a cornerstone in the transition from simple fact-retrieval to sophisticated AI reasoning. By forcing models to navigate complex Wikipedia structures and synthesize answers, datasets like NQ and its derivatives like CLAPnq are essential for building the next generation of reliable, accurate digital assistants. Scopus | Abstract and citation database - Elsevier ntq.rar

Benchmarking the Future: The Evolution of Natural Questions (NQ) and RAG Systems 1. Introduction to Natural Questions (NQ) : Identifying when a provided document does not

: Ensuring answers are grounded strictly in the provided text without "hallucinations". Scopus | Abstract and citation database - Elsevier

: Remaining "grounded" to the document rather than relying on internal (and potentially outdated) training data. 4. Conclusion

While traditional NQ focused on short, few-word answers, modern research has shifted toward . This has led to the development of CLAPnq (Cohesive Long-form Answers from Passages) , a benchmark that uses NQ data to test whether LLMs can provide:

: Combining multiple, non-contiguous parts of a document into a single fluid response.