# ELI5: Long Form Question Answering

arXiv: https://arxiv.org/abs/1907.09190 · HTML: https://mlsanity.com/paper/1907.09190/

## Key facts

- Cited by 575 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2019.

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## 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.