mlsanityData to 7 Oct 2026

ELI5: Long Form Question Answering

arXiv 1907.09190 ↗First posted 22 Jul 2019cs.CLcited by 567

Not on the map. Why?

Cited by 91 in Retrieval-augmented generation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce the first large-scale corpus for long-form question answering, a task requiring elaborate and in-depth answers to open-ended questions. The dataset comprises 270K threads from the Reddit forum ``Explain Like I'm Five'' (ELI5) where an online community provides answers to questions which are comprehensible by five year olds. Compared to existing datasets, ELI5 comprises diverse questions requiring multi-sentence answers. We provide a large set of web documents to help answer the question. Automatic and human evaluations show that an abstractive model trained with a multi-task objective outperforms conventional Seq2Seq, language modeling, as well as a strong extractive baseline. However, our best model is still far from human performance since raters prefer gold responses in over 86% of cases, leaving ample opportunity for future improvement.

Key facts. Cited by 567 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jul 2019.

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Citing papers per year: 2020: 1; 2021: 41; 2022: 52; 2023: 104; 2024: 162; 2025: 134; 2026: 73.

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  17. 2021The GEM Benchmark: Natural Language Generation, its Evaluation and Metricscited by 239
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