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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Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
(2004) · Cited by 9,718
Enriching Word Vectors with Subword Information
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00051 · Cited by 9,645
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208
Billion-scale similarity search with GPUs
Johnson, J., Douze, M., & Jegou, H. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2921572 · Cited by 4,228
Convolutional Sequence to Sequence Learning
Gehring, J., Auli, M., Grangier, D., Yarats, D., & Dauphin, Y. N. (2017) · arXiv · doi:10.48550/arxiv.1705.03122 · Cited by 2,850 · cited 2×
“We use the Transformer implementation of fairseq-py (Gehring et al., 2017) and train with the big architecture following the details in (Vaswani et al.”
MethodSelf-citationMS MARCO: A Human Generated MAchine Reading COmprehension Dataset
Bajaj, P., Campos, D., Craswell, N., Deng, L., Gao, J., Liu, X., Majumder, R., McNamara, A., Mitra, B., Nguyen, T., et al. (2016) · arXiv · doi:10.48550/arxiv.1611.09268 · Cited by 2,473
Know What You Don’t Know: Unanswerable Questions for SQuAD
Rajpurkar, P., Jia, R., & Liang, P. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p18-2124 · Cited by 2,240
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W., Salakhutdinov, R., & Manning, C. D. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1259 · Cited by 1,804
Bidirectional Attention Flow for Machine ComprehensionPreprint
Seo, M. J., Kembhavi, A., Farhadi, A., & Hajishirzi, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01603 · Cited by 1,698
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Joshi, M., Choi, E., Weld, D., & Zettlemoyer, L. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1147 · Cited by 1,690
Reading Wikipedia to answer open-domain questions
Chen, D., Fisch, A., Weston, J., & Bordes, A. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1171 · Cited by 1,546
Self-citationA Deep Reinforced Model for Abstractive Summarization
Paulus, R., Xiong, C., & Socher, R. (2017) · arXiv · doi:10.48550/arxiv.1705.04304 · Cited by 1,182
CoQA: A Conversational Question Answering Challenge
Reddy, S., Chen, D., & Manning, C. D. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00266 · Cited by 975
Generating Wikipedia by Summarizing Long SequencesPreprint
Liu, P. J., Saleh, M., Pot, E., Goodrich, B., Sepassi, R., Kaiser, Ł., & Shazeer, N. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.10198 · Cited by 784
QuAC: Question Answering in Context
Choi, E., He, H., Iyyer, M., Yatskar, M., Yih, W.-T., Choi, Y., Liang, P., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1241 · Cited by 714 · cited 3×
“Extractive QA Extractive question answering datasets such as TREC (Voorhees, 2003), SQuAD (Rajpurkar et al., 2016, 2018), NewsQA (Trischler et al., 2017), SearchQA (Dunn et al., 2017), and QuAC (Choi et al., 2018) constrain the answer to a word or short phrase from the input and evaluate using exact match or F1 with t…”
BackgroundMethodNewsQA: A Machine Comprehension Dataset
Trischler, A., Wang, T., Yuan, X., Harris, J., Sordoni, A., Bachman, P., & Suleman, K. (2017) · Proceedings of the 2nd Workshop on Representation Learning for NLP · doi:10.18653/v1/w17-2623 · Cited by 706 · cited 3×
“Extractive QA Extractive question answering datasets such as TREC (Voorhees, 2003), SQuAD (Rajpurkar et al., 2016, 2018), NewsQA (Trischler et al., 2017), SearchQA (Dunn et al., 2017), and QuAC (Choi et al., 2018) constrain the answer to a word or short phrase from the input and evaluate using exact match or F1 with t…”
MethodScaling Neural Machine Translation
Ott, M., Edunov, S., Grangier, D., & Auli, M. (2018) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w18-6301 · Cited by 631
Self-citationThe NarrativeQA Reading Comprehension Challenge
Kočiský, T., Schwarz, J., Blunsom, P., Dyer, C., Hermann, K. M., Melis, G., & Grefenstette, E. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00023 · Cited by 596 · cited 2×
“Abstractive QA Abstractive datasets include NarrativeQA (Kocisky et al., 2018), a dataset of movie and book summaries and CoQA (Reddy et al.”
BackgroundMethodSearchQA: A New Q&A Dataset Augmented with Context from a Search Engine
Dunn, M., Sagun, L., Higgins, M., Guney, V. U., Cirik, V., & Cho, K. (2017) · arXiv · doi:10.48550/arxiv.1704.05179 · Cited by 359
Controllable Abstractive Summarization
Fan, A., Grangier, D., & Auli, M. (2018) · Proceedings of the 2nd Workshop on Neural Machine Translation and Generation · doi:10.18653/v1/w18-2706 · Cited by 281 · cited 2×
“nique commonly used in multi-sentence summarization (Paulus et al., 2017; Fan et al., 2018).”
MethodSelf-citationAdvantages of query biased summaries in information retrieval
Tombros, A., & Sanderson, M. (1998) · Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/290941.290947 · Cited by 241
QuAC : Question Answering in ContextPreprint
Choi, E., He, H., Iyyer, M., Yatskar, M., Yih, W.-T., Choi, Y., Liang, P., & Zettlemoyer, L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.07036 · Cited by 209
Making Neural QA as Simple as Possible but not Simpler
Weissenborn, D., Wiese, G. K., & Seiffe, L. (2017) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k17-1028 · Cited by 168
Research works printed but not linked: 2
- Myle Ott, Sergey Edunov, David Grangier, and Michael Auli. 2018. Scaling neural machine trans- lation. In WMT, pages 1-9. Association for Compu- tational Linguistics.
- Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Neural machine translation of rare words with subword units. In ACL.
Cited by
Sorted by citations
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821
Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.11401 · Cited by 3,101
Self-citationHuang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al. (2025) · ACM Transactions on Information Systems · doi:10.1145/3703155 · Cited by 4,688
“However, when confronted with intricate challenges like multi-step reasoning (Yang et al., 2018c) and long-form question answering (Fan et al., 2019; Stelmakh et al., 2022), traditional one-time retrieval may fall short.”
A Comprehensive Overview of Large Language Models
Naveed, H., Khan, A. U., Qiu, S., Saqib, M., Anwar, S., Usman, M., Akhtar, N., Barnes, N., & Mian, A. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3744746 · Cited by 1,257
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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