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:
: Distilling large passages into grounded answers that are often three times smaller than the source. 3. Key Challenges in Long-form QA (LFQA) ntq.rar
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 While traditional NQ focused on short, few-word answers,