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Long Form Question Answering datasets

archive 2025-07-28

5 datasets carry the task tag "Long Form Question Answering" (the task itself: Long Form Question Answering), ordered by the archive's paper count. Page 1 of 1: 5 shown of 5. Facet routes are this site's own (the archive records the tag string, not a page).

The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.

Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets

Long Form Question Answering datasets 1–5 of 5

ELI5 is a dataset for long-form question answering.
158 papers · 1 benchmark
QuALITY (Question Answering with Long Input Texts, Yes!)
QuALITY (Question Answering with Long Input Texts, Yes!) is a multiple-choice question answering dataset for long document comprehension.
98 papers · 1 benchmark
LLeQA (Long-form Legal Question Answering)
LLeQA is a French native dataset for studying information retrieval and long-form question answering in the legal domain.
3 papers · 0 benchmarks
DARai (Daily Activity Recordings for AI and ML applications)
Daily Activity Recordings for Artificial Intelligence (DARai, pronounced "Dahr-ree") is a multimodal, hierarchically annotated dataset constructed to understand human activities in real-world settings.
1 paper · 0 benchmarks
MMInstruct-GPT4V (MMInstruct: A High-Quality Multi-Modal Instruction Tuning Dataset with Extensive Diversity)
Vision-language supervised fine-tuning effectively enhances VLLM performance, but existing visual instruction tuning datasets have limitations: 1.
1 paper · 0 benchmarks

Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.