{"url":"/dataset/loong","name":"Loong","full_name":"Loong","description_markdown":"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.","description_withheld":null,"homepage":"https://github.com/MozerWang/Loong","introduced_date":"2024-06-25","introduced_date_note":null,"introduced_by":{"paper":"/paper/leave-no-document-behind-benchmarking-long","title":"Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA","first_author":"Minzheng Wang","url":null},"license":{"name":"Apache-2.0","url":"https://opensource.org/license/apache-2-0"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[],"languages":[{"name":"English","url":"/datasets/language/english"},{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["Loong"],"data_loaders":[],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}