The DOI matches its Crossref record; 26 of the 28 references it cites were found in a registry or an index.
ELI5: Long Form Question Answering
Open accessAbstract
We introduce the first large-scale corpus for long-form question answering, a task requiring elaborate and in-depth answers to openended 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.1
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“Recent years have seen the emergence of long-form QA evaluations, such as ELI5 (Fan et al., 2019) and ASQA (Stelmakh et al., 2022a), which allow for more elaborate answers.”
From DebateQA: Evaluating Question Answering on Debatable Knowledge (2026) · cites this paper as Fan et al., 2019
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“LFQA (Chen et al., 2023) contains LLMgenerated responses to questions from the ELI5 (”
From MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents (2024) · cites this paper as Fan et al., 2019
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“WebGPT (Nakano et al., 2021) uses an LM to interact with web browsers, navigate through web pages, and infer answers to complicated questions from ELI5 (Fan et al., 2019).”
From ReAct: Synergizing Reasoning and Acting in Language Models (2022) · cites this paper as Fan et al., 2019
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Self-citation
Metadata
- Authors
- Fan, A., Jernite, Y., Perez, E., Grangier, D., Weston, J., & Auli, M.
- Affiliations
- Meta (United States) (US)
- New York University (US)
- Venue
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Published
- 2019
- Pages
- 3558-3567
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2950681488
- Cited by
- 319
- References
- 28
- Citations by year
- 2026: 27 · 2025: 59 · 2024: 35 · 2023: 66 · 2022: 62 · 2021: 42 · 2020: 21 · 2019: 7
- FWCI
- 12.63
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- long-form question answering, Reddit, corpus construction, abstractive summarization, open-ended questions, multi-task learning, human evaluation
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