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ELI5: Long Form Question Answering
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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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Ahuja, O., Xu, J., Gupta, A., Horecka, K. M., & Durrett, G. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.449 · Cited by 38
An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Tasks
Wu, Y., Zhao, Y. Q., Hu, B., Minervini, P., Stenetorp, P., & Riedel, S. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.346 · Cited by 57
A Critical Evaluation of Evaluations for Long-form Question Answering
Xu, F., Song, Y., Iyyer, M., & Choi, E. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.181 · Cited by 33
Read before Generate! Faithful Long Form Question Answering with Machine Reading
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Chen, J., Zhang, R., Guo, J., de Rijke, M., Liu, Y., Fan, Y., & Cheng, X. (2023) · Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3539618.3591631 · Cited by 32
WebCPM: Interactive Web Search for Chinese Long-form Question Answering
Qin, Y., Cai, Z., Jin, D., Yan, L., Liang, S., Zhu, K., Lin, Y., Han, X., Ding, N., Wang, H., et al. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.499 · Cited by 78
KI-MAG: A knowledge-infused abstractive question answering system in medical domain
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Controllable Open-ended Question Generation with A New Question Type Ontology
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Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge
Jang, Y., Lim, J., Hur, Y., Oh, D., Son, S., Lee, Y., Shin, D., Kim, S., & Lim, H. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21326 · Cited by 39
MixQG: Neural Question Generation with Mixed Answer Types
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Review on Neural Question Generation for Education Purposes
Faraby, S. A., Adiwijaya, A., & Romadhony, A. (2023) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00374-x · Cited by 44
Zero-shot Generalization in Dialog State Tracking through Generative Question Answering
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D2S: Document-to-Slide Generation Via Query-Based Text Summarization
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Nonfactoid Question Answering as Query-Focused Summarization With Graph-Enhanced Multihop Inference
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Truncation Sampling as Language Model Desmoothing
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RA-DIT: Retrieval-Augmented Dual Instruction Tuning
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Detection of AI-Generated Text Using Large Language Model
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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