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
According to researchers from the ACL Anthology , LLMs still face significant hurdles in these areas: ntq.rar
Benchmarking the Future: The Evolution of Natural Questions (NQ) and RAG Systems 1. Introduction to Natural Questions (NQ) The Natural Questions (NQ) dataset, originally released by
: Ensuring answers are grounded strictly in the provided text without "hallucinations". : Distilling large passages into grounded answers that
: Distilling large passages into grounded answers that are often three times smaller than the source. 3. Key Challenges in Long-form QA (LFQA)