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TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
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We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples.TriviaQA includes 95K questionanswer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions.We show that, in comparison to other recently introduced large-scale datasets, TriviaQA (1) has relatively complex, compositional questions, (2) has considerable syntactic and lexical variability between questions and corresponding answer-evidence sentences, and (3) requires more cross sentence reasoning to find answers.We also present two baseline algorithms: a featurebased classifier and a state-of-the-art neural network, that performs well on SQuAD reading comprehension.Neither approach comes close to human performance (23% and 40% vs. 80%), suggesting that Trivi-aQA is a challenging testbed that is worth significant future study. 1
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“While past benchmarks have measured the ability to retrieve information (Joshi et al., 2017;Yang et al., 2018;Thorne et al., 2018;Dinan et al., 2019;Fan et al., 2019;Mialon et al., 2023), most of these benchmarks focus on retrieving information that can be found easily, and hence have become saturated by recent language models.”
From BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents (2025) · cites this paper as Joshi et al., 2017
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“However, these benchmarks primarily feature single-hop questions, which typically do not require multi-step reasoning or iterative retrieval.”
From BrowseComp-Plus: A Fair and Disentangled Evaluation Benchmark for Deep Search Agents (2026) · cites this paper as Joshi et al., 2017
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“On TriviaQA (Joshi et al., 2017) we show results for both the filtered (previously used in retrieval and open-book work) and unfiltered set (previously used in large language model evaluations). In both cases, Chinchilla substantially out performs Gopher .”
From Training Compute-Optimal Large Language Models (2022) · cites this paper as Joshi et al., 2017
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“strongly outperform recent approaches that use specialised pre-training objectives on TriviaQA [24].”
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“One reason to focus on imitative falsehoods is that they are less likely to be covered by existing question-answering benchmarks (Clark et al., 2018; Kwiatkowski et al., 2019; Joshi et al., 2017; Hendrycks et al., 2020).”
From TruthfulQA: Measuring How Models Mimic Human Falsehoods (2022) · cites this paper as Joshi et al., 2017
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“LLMs perform better on nearly all datasets, such as on NaturalQuestions [62], WebQuestions [10], and TriviaQA [56].”
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“Data : We construct a changing-prior QA dataset from TriviaQA (Joshi et al., 2017) by categorizing questions into three classes, Person, Location, and Other, based on what kind of entity they are asking about.”
MethodInfluentialSelf-citationBoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., & Toutanova, K. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1300 · Cited by 655
Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences
Khashabi, D., Chaturvedi, S., Roth, M., Upadhyay, S., & Roth, D. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1023 · Cited by 407
Entity-Relation Extraction as Multi-Turn Question Answering
Li, X., Yin, F., Sun, Z., Li, X., Yuan, A., Chai, D., Zhou, M., & Li, J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1129 · Cited by 431
PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text
Sun, H., Bedrax-Weiss, T., & Cohen, W. W. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1242 · Cited by 443
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
Xiong, L., Xiong, C., Li, Y., Tang, K., Liu, J., Bennett, P. N., Ahmed, J., & Overwikj, A. (2021) · International Conference on Learning Representations · Cited by 376
ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction
Santhanam, K., Khattab, O., Saad-Falcon, J., Potts, C., & Zaharia, M. (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.272 · Cited by 353
Clark, J. H., Choi, E., Collins, M., Garrette, D., Kwiatkowski, T., Nikolaev, V., & Palomaki, J. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00317 · Cited by 477
Self-citationKILT: a Benchmark for Knowledge Intensive Language Tasks
Petroni, F., Piktus, A., Fan, A., Lewis, P., Yazdani, M., De Cao, N., Thorne, J., Jernite, Y., Karpukhin, V., Maillard, J., et al. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.200 · Cited by 357
LinkBERT: Pretraining Language Models with Document Links
Yasunaga, M., Leskovec, J., & Liang, P. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.551 · Cited by 474
Siriwardhana, S., Weerasekera, R., Wen, E., Kaluarachchi, T., Rana, R., & Nanayakkara, S. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00530 · Cited by 337
ELI5: long form question answering
Fan, A., Jernite, Y., Perez, E., Grangier, D., Weston, J., & Auli, M. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1346 · Cited by 319
Survey on evaluation methods for dialogue systems
Deriu, J., Rodrigo, A., Otegi, A., Echegoyen, G., Rosset, S., Agirre, E., & Cieliebak, M. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09866-x · Cited by 361
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis
Xu, H., Liu, B., Shu, L., & Yu, P. S. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1242 · Cited by 321
CommonsenseQA: A Question Answering Challenge Targeting Commonsense KnowledgePreprint
Talmor, A., Herzig, J., Lourie, N., & Berant, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.00937 · Cited by 822
R3: Reinforced Ranker-Reader for Open-Domain Question Answering
Wang, S., Yu, M., Guo, X., Wang, Z., Klinger, T., Zhang, W., Chang, S., Tesauro, G., Zhou, B., & Jiang, J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12053 · Cited by 351
How Much Knowledge Can You Pack into the Parameters of a Language Model?
Roberts, A., Raffel, C., & Shazeer, N. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.437 · Cited by 638
DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension
Sun, K., Yu, D., Chen, J., Yu, D., Choi, Y., & Cardie, C. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00264 · Cited by 252
Deep Reinforcement Learning for Sequence-to-Sequence Models
Keneshloo, Y., Shi, T., Ramakrishnan, N., & Reddy, C. K. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2929141 · Cited by 230
Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering
Wang, Z., Ng, P., Ma, X., Nallapati, R. M., & Xiang, B. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1599 · Cited by 277
LongT5: Efficient Text-To-Text Transformer for Long Sequences
Guo, M., Ainslie, J., Uthus, D., Ontanon, S., Ni, J., Sung, Y.-H., & Yang, Y. (2022) · Findings of the Association for Computational Linguistics: NAACL 2022 · doi:10.18653/v1/2022.findings-naacl.55 · Cited by 230
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
Xiong, L., Xiong, C., Li, Y., Tang, K.-F., Liu, J., Bennett, P., Ahmed, J., & Overwijk, A. (2020) · arXiv · doi:10.48550/arxiv.2007.00808 · Cited by 204
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
He, W. P., Liu, K., Liu, J., Lyu, Y., Zhao, S., Xiao, X., Liu, Y., Wang, Y., Wu, H. R., She, Q., et al. (2018) · Workshop on Machine Reading for Question Answering (MRQA) · doi:10.18653/v1/w18-2605 · Cited by 244
Autoregressive Entity RetrievalPreprint
Cao, N. D., Izacard, G., Riedel, S., & Petroni, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.00904 · Cited by 384
MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension
Fisch, A., Talmor, A., Jia, R., Seo, M., Choi, E., & Chen, D. (2019) · Workshop on Machine Reading for Question Answering (MRQA) · doi:10.18653/v1/d19-5801 · Cited by 254
Self-citationA Survey of Knowledge Enhanced Pre-Trained Language Models
Hu, L., Liu, Z., Zhao, Z., Hou, L., Nie, L., & Li, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3310002 · Cited by 246
What large language models know and what people think they know
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Neural Approaches to Conversational AI
Gao, J., Galley, M., & Li, L. (2019) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000074 · Cited by 424
REPLUG: Retrieval-Augmented Black-Box Language Models
Shi, W., Min, S., Yasunaga, M., Seo, M., James, R., Lewis, M., Zettlemoyer, L., & Yih, W.-T. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2024.naacl-long.463 · Cited by 209
Self-citationGraph Retrieval-Augmented Generation: A Survey
Peng, B., Zhu, Y., Liu, Y., Bo, X., Shi, H., Hong, C., Zhang, Y., & Tang, S. (2026) · ACM Transactions on Information Systems · doi:10.1145/3777378 · Cited by 206
HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data
Chen, W., Zha, H., Chen, Z., Xiong, W., Wang, H., & Wang, W. Y. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.91 · Cited by 290
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No QuestionsPreprint
Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., & Toutanova, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.10044 · Cited by 1,070
SG-Net: Syntax-Guided Machine Reading Comprehension
Zhang, Z., Wu, Y., Zhou, J., Duan, S., Zhao, H. Y., & Wang, R. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6511 · Cited by 198
ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading ComprehensionPreprint
Zhang, S., Liu, X., Liu, J., Gao, J., Duh, K., & Durme, B. V. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.12885 · Cited by 339
Multi-hop Reading Comprehension through Question Decomposition and Rescoring
Min, S., Zhong, V., Zettlemoyer, L., & Hajishirzi, H. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1613 · Cited by 197
Self-citationMihaylov, T., & Frank, A. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1076 · Cited by 205
Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval
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Generative AI in Higher Education: The ChatGPT Effect
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Retrospective Reader for Machine Reading Comprehension
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AmbigQA: Answering Ambiguous Open-domain Questions
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Self-citationemrQA: A Large Corpus for Question Answering on Electronic Medical Records
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Event Extraction as Multi-turn Question Answering
Li, F., Peng, W., Chen, Y., Wang, Q., Pan, L., Lyu, Y., & Zhu, Y. T. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.73 · Cited by 191
Muppet: Massive Multi-task Representations with Pre-Finetuning
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Self-citationSPoT: Better Frozen Model Adaptation through Soft Prompt Transfer
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Editing Factual Knowledge in Language Models
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Training Compute-Optimal Large Language Models
Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. D. L., Hendricks, L. A., Welbl, J., Clark, A., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-2176 · Cited by 932
“, 2019) and TriviaQA (Joshi et al., 2017), Chinchilla outperforms Gopher in all cases.”
Influential
Metadata
- Authors
- Joshi, M., Choi, E., Weld, D., & Zettlemoyer, L.
- Affiliations
- University of Washington (US)
- Allen Institute for Artificial Intelligence (US)
- Venue
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2017
- Pages
- 1601-1611
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2612431505
- Cited by
- 1,690
- References
- 21
- Citations by year
- 2026: 141 · 2025: 205 · 2024: 157 · 2023: 220 · 2022: 204 · 2021: 259 · 2020: 198 · 2019: 190 · 2018: 97 · 2017: 19
- FWCI
- 61.63
- 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)
- IEEE DataPort (cc-by)
- Funders
- Tencent
- Defense Advanced Research Projects Agency (FA8750-13-2-0019, FA8750)
- 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
- TriviaQA, reading comprehension, distant supervision, trivia questions, SQuAD
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.
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