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

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

According to researchers from the ACL Anthology , LLMs still face significant hurdles in these areas: : Ensuring answers are grounded strictly in the

: Remaining "grounded" to the document rather than relying on internal (and potentially outdated) training data. 4. Conclusion Scopus | Abstract and citation database - Elsevier

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: