Omkar2118/datasets huggingface dataset:
Loong
Full name: Loong

We propose a novel long-context benchmark, 🐉 Loong, aligning with realistic scenarios through extended multi-document question answering (QA). Loong typically consists of 11 documents per test instance on average, spanning three real-world scenarios in English and Chinese: (1) Financial Reports, (2) Legal Cases, and (3) Academic Papers. Meanwhile, Loong introduces new evaluation tasks from the perspectives of Spotlight Locating, Comparison, Clustering, and Chain of Reasoning, to facilitate a more realistic and comprehensive evaluation of long-context understanding. Furthermore, Loong features inputs of varying lengths (e.g., 10K-50K, 50K-100K, 100K-200K, beyond 200K) and evaluation tasks of diverse difficulty, enabling fine-grained assessment of LLMs across different context lengths and task complexities.

Introducing paper: Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA