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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:

According to researchers from the ACL Anthology , LLMs still face significant hurdles in these areas:

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

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

: Remaining "grounded" to the document rather than relying on internal (and potentially outdated) training data. 4.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

: Distilling large passages into grounded answers that are often three times smaller than the source. 3. Key Challenges in Long-form QA (LFQA)

Benchmarking the Future: The Evolution of Natural Questions (NQ) and RAG Systems 1. Introduction to Natural Questions (NQ) Unlike synthetic datasets, NQ consists of real queries

: Ensuring answers are grounded strictly in the provided text without "hallucinations".