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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 contain the answer is a critical real-world skill that models still struggle with. ntq.rar

: Remaining "grounded" to the document rather than relying on internal (and potentially outdated) training data. 4. Conclusion Benchmarking the Future: The Evolution of Natural Questions

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: Unlike synthetic datasets, NQ consists of real queries

The Natural Questions (NQ) dataset, originally released by researchers at Google, revolutionized how AI models handle information retrieval. Unlike synthetic datasets, NQ consists of real queries typed into Google Search, paired with entire Wikipedia pages as the source of truth. This creates a "real-world" challenge: models must not only find the right document but also extract a concise, human-like answer from within it. 2. The Shift to RAG and CLAPnq