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 long-form QA evaluations such as ELI5 and ASQA allow for more elaborate answers.
“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
LFQA contains LLMgenerated responses to questions from the ELI5 dataset.
“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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Evaluating the Evaluation of Diversity in Natural Language Generation
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QuALITY: Question Answering with Long Input Texts, Yes!
Pang, R. Y., Parrish, A., Joshi, N., Nangia, N., Phang, J., Chen, A., Padmakumar, V., Ma, J., Thompson, J., He, H., et al. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.391 · Cited by 51 · see citing works
Towards Continual Knowledge Learning of Language Models
Jang, J., Ye, S., Yang, S., Shin, J., Han, J., Kim, G., Choi, S. J., & Seo, M. (2021) · arXiv · doi:10.48550/arxiv.2110.03215 · Cited by 49 · see citing works
“Configuration KILT (Petroni et al., 2021) consists of 5 different tasks and 11 datasets: OpenDomain Question Answering (Joshi et al., 2017; Kwiatkowski et al., 2019; Fan et al., 2019; Yang et al., 2018), Fact Checking (Thorne et al., 2018), Entity Linking (Hoffart et al., 2011; Guo & Barbosa, 2018), Slot-filling (Levy…”
BackgroundWebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences
Liu, X., Lai, H., Yu, H., Xu, Y., Zeng, A., Du, Z., Zhang, P., Dong, Y., & Tang, J. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599931 · Cited by 64 · see citing works
Visual language navigation: a survey and open challenges
Park, S., & Kim, Y. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10174-9 · Cited by 45 · see citing works
Task-aware Retrieval with Instructions
Asai, A., Schick, T., Lewis, P., Chen, X., Izacard, G., Riedel, S., Hajishirzi, H., & Yih, W.-T. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.225 · Cited by 46 · see citing works
Toward Personalized Answer Generation in E-Commerce via Multi-perspective Preference Modeling
Deng, Y., Li, Y., Zhang, W., Ding, B., & Lam, W. P. (2022) · ACM Transactions on Information Systems · doi:10.1145/3507782 · Cited by 43 · see citing works
Fabbri, A. R., Rahman, F., Rizvi, I., Wang, B., Li, H., Mehdad, Y., & Radev, D. (2021) · doi:10.18653/v1/2021.acl-long.535 · Cited by 45 · see citing works
Generating fact checking briefs
Fan, A., Piktus, A., Petroni, F., Wenzek, G., Saeidi, M., Vlachos, A., Bordes, A., & Riedel, S. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.580 · Cited by 42 · see citing works
Self-citationR4C: A Benchmark for Evaluating RC Systems to Get the Right Answer for the Right Reason
Inoue, N., Stenetorp, P., & Inui, K. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.602 · Cited by 43 · see citing works
Smith, E., Hsu, O., Qian, R., Roller, S., Boureau, Y.-L., & Weston, J. (2022) · Proceedings of the 4th Workshop on NLP for Conversational AI · doi:10.18653/v1/2022.nlp4convai-1.8 · Cited by 40 · see citing works
Self-citationEmpower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering
Yang, F., Zhao, P., Wang, Z., Wang, L., Qiao, B., Zhang, J., Garg, M., Lin, Q., Rajmohan, S., & Zhang, D. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-industry.29 · Cited by 53 · see citing works
HaluCheck: Explainable and verifiable automation for detecting hallucinations in LLM responses
Heo, S., Son, S., & Park, H. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126712 · Cited by 47 · see citing works
Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions
Roller, S., Boureau, Y.-L., Weston, J., Bordes, A., Dinan, E., Fan, A., Gunning, D., Ju, D., Li, M., Poff, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2006.12442 · Cited by 38 · see citing works
Self-citationA review of public datasets in question answering research
Cambazoglu, B. B., Sanderson, M., Scholer, F., & Croft, B. W. (2020) · ACM SIGIR Forum · doi:10.1145/3483382.3483389 · Cited by 36 · see citing works
GooAQ: Open Question Answering with Diverse Answer Types
Khashabi, D., Ng, A., Khot, T., Sabharwal, A., Hajishirzi, H., & Callison-Burch, C. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.38 · Cited by 37 · see citing works
A Non-Factoid Question-Answering Taxonomy
Baranova-Bolotova, V., Blinov, V., Scholer, F., Croft, W. B., & Sanderson, M. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531926 · Cited by 35 · see citing works
ASPECTNEWS: Aspect-Oriented Summarization of News Documents
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 36 · see citing works
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 39 · see citing works
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 · see citing works
Read before Generate! Faithful Long Form Question Answering with Machine Reading
Su, D., Li, X., Zhang, J., Shang, L., Jiang, X., Liu, Q., & Fung, P. (2022) · Findings of the Association for Computational Linguistics: ACL 2022 · doi:10.18653/v1/2022.findings-acl.61 · Cited by 37 · see citing works
A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning
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 · see citing works
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 50 · see citing works
KI-MAG: A knowledge-infused abstractive question answering system in medical domain
Zafar, A., Sahoo, S. K., Bhardawaj, H., Das, A., & Ekbal, A. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.127141 · Cited by 33 · see citing works
Darshan, B. S. D., Sampathila, N., Bairy, G. M., Prabhu, S., Belurkar, S., Chadaga, K., & Nandish, S. (2025) · Scientific Reports · doi:10.1038/s41598-024-84120-w · Cited by 32 · see citing works
Controllable Open-ended Question Generation with A New Question Type Ontology
Cao, S., & Wang, L. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.502 · Cited by 35 · see citing works
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 34 · see citing works
MixQG: Neural Question Generation with Mixed Answer Types
Murakhovs’ka, L., Wu, C.-S., Laban, P., Niu, T., Liu, W., & Xiong, C. (2022) · Findings of the Association for Computational Linguistics: NAACL 2022 · doi:10.18653/v1/2022.findings-naacl.111 · Cited by 31 · see citing works
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 31 · see citing works
Zero-shot Generalization in Dialog State Tracking through Generative Question Answering
Li, S., Cao, J. L., Sridhar, M., Zhu, H., Li, S.-W. D., Hamza, W., & McAuley, J. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.91 · Cited by 35 · see citing works
D2S: Document-to-Slide Generation Via Query-Based Text Summarization
Sun, E., Hou, Y., Wang, D., Zhang, Y., & Wang, N. X. R. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.111 · Cited by 36 · see citing works
Chen, H.-T., Zhang, M., & Choi, E. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.146 · Cited by 31 · see citing works
Chen, H.-T., Zhang, M. J. Q., & Choi, E. (2022) · arXiv · doi:10.48550/arxiv.2210.13701 · Cited by 31 · see citing works
Nonfactoid Question Answering as Query-Focused Summarization With Graph-Enhanced Multihop Inference
Deng, Y., Zhang, W., Xu, W., Shen, Y., & Lam, W. P. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3258413 · Cited by 30 · see citing works
Red Teaming Language Model Detectors with Language Models
Shi, Z., Wang, Y., Yin, F., Chen, X., Chang, K.-W., & Hsieh, C. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00639 · Cited by 39 · see citing works
Comparative analysis of text-based plagiarism detection techniques
Sajid, M., Sanaullah, M., Fuzail, M., Malik, T. S., & Shuhidan, S. M. (2025) · PLoS ONE · doi:10.1371/journal.pone.0319551 · Cited by 30 · see citing works
MultiSpanQA: A Dataset for Multi-Span Question Answering
Li, H., Tomko, M., Vasardani, M., & Baldwin, T. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.90 · Cited by 30 · see citing works
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering
Riabi, A., Scialom, T., Keraron, R., Sagot, B., Seddah, D., & Staiano, J. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.562 · Cited by 30 · see citing works
AI-Generated Text Detector for Arabic Language Using Encoder-Based Transformer Architecture
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Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering
Kim, N., Pavlick, E., Ayan, B. K., & Ramachandran, D. (2021) · doi:10.18653/v1/2021.acl-long.304 · Cited by 30 · see citing works
Vaccine Hesitancy Hotspots in Africa: An Insight From Geotagged Twitter Posts
Ogbuokiri, B., Ahmadi, A., Nia, Z. M., Garcia, B. R. M., Wu, J., Orbinski, J., Asgary, A., & Kong, J. D. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3236368 · Cited by 26 · see citing works
Truncation Sampling as Language Model Desmoothing
Hewitt, J., Manning, C. D., & Liang, P. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.249 · Cited by 31 · see citing works
CREAK: A Dataset for Commonsense Reasoning over Entity KnowledgePreprint
Onoe, Y., Zhang, M. J. Q., Choi, E., & Durrett, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.01653 · Cited by 26 · see citing works
RA-DIT: Retrieval-Augmented Dual Instruction Tuning
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DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization
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Modeling Multi-hop Question Answering as Single Sequence Prediction
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BREE-HD: A Transformer-Based Model to Identify Threats on Twitter
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Detection of AI-Generated Text Using Large Language Model
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Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study
Wang, B., Ping, W., Xu, P., McAfee, L., Liu, Z., Shoeybi, M., Dong, Y., Kuchaiev, O., Li, B., Xiao, C., et al. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.482 · Cited by 21 · see citing works
- 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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