: Identifying when a provided document does not contain the answer is a critical real-world skill that models still struggle with.
: Ensuring answers are grounded strictly in the provided text without "hallucinations". ntq.rar
: Remaining "grounded" to the document rather than relying on internal (and potentially outdated) training data. 4. Conclusion : Identifying when a provided document does not
According to researchers from the ACL Anthology , LLMs still face significant hurdles in these areas: Key Challenges in Long-form QA (LFQA) While traditional
: Distilling large passages into grounded answers that are often three times smaller than the source. 3. Key Challenges in Long-form QA (LFQA)
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: