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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].”
From Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (2020) · cites this paper as [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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Yang, D., Zeng, L., Rao, J., & Zhang, Y. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730018 · Cited by 21
“For single-hop QA, we use TriviaQA [15], a widely used benchmark that focuses on factoid questions requiring reasoning over a single piece of evidence from Wikipedia.”
Translucent Answer Predictions in Multi-Hop Reading Comprehension
Bhargav, G. P. S., Glass, M., Garg, D., Shevade, S., Dana, S., Khandelwal, D., Subramaniam, L. V., & Gliozzo, A. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6272 · Cited by 14
Progressively Pretrained Dense Corpus Index for Open-Domain Question Answering
Xiong, W., Wang, H., & Wang, W. Y. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.244 · Cited by 14
Data Augmentation for Biomedical Factoid Question Answering
Pappas, D., Malakasiotis, P., & Androutsopoulos, I. (2022) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2022.bionlp-1.6 · Cited by 19
Talk to Papers: Bringing Neural Question Answering to Academic Search
Zhao, T., & Lee, K. (2020) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.18653/v1/2020.acl-demos.5 · Cited by 13
You Don’t Need Labeled Data for Open-Book Question Answering
Gholami, S., & Noori, M. (2021) · Applied Sciences · doi:10.3390/app12010111 · Cited by 20
Evaluating Semantic Parsing against a Simple Web-based Question Answering Model
Talmor, A., Geva, M., & Berant, J. (2017) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/s17-1020 · Cited by 14
Weaver: Deep Co-Encoding of Questions and Documents for Machine ReadingPreprint
Raison, M., Mazaré, P.-E., Das, R., & Bordes, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.10490 · Cited by 15
Answering Science Exam Questions Using Query Rewriting with Background KnowledgePreprint
Musa, R., Wang, X., Fokoue, A., Mattei, N., Chang, M., Kapanipathi, P., Makni, B., Talamadupula, K., & Witbrock, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.05726 · Cited by 11
An Atlas of Cultural Commonsense for Machine Reasoning.Preprint
Acharya, A., Talamadupula, K., & Finlayson, M. A. (2020) · arXiv (Cornell University) · Cited by 12
RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question AnsweringPreprint
Iyer, S. V., Min, S., Mehdad, Y., & Yih, W.-T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.10757 · Cited by 13
KgPLM: Knowledge-guided Language Model Pre-training via Generative and Discriminative LearningPreprint
He, B., Jiang, X., Xiao, J., & Liu, Q. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.03551 · Cited by 17
Labutov, I., Yang, B., Prakash, A., & Azaria, A. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1077 · Cited by 11
Multi-Granular Sequence Encoding via Dilated Compositional Units for Reading Comprehension
Tay, Y., Luu, A. T., & Hui, S. C. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1238 · Cited by 10
A Knowledge Graph Embedding Model for Answering Factoid Entity Questions
Jafarzadeh, P., Ensan, F., Alavi, M. A. A., & Zarrinkalam, F. (2024) · ACM Transactions on Information Systems · doi:10.1145/3678003 · Cited by 12
Srivastava, A., & Memon, A. M. (2024) · IEEE Access · doi:10.1109/access.2024.3446854 · Cited by 14
ConFit: Improving Resume-Job Matching using Data Augmentation and Contrastive Learning
Yu, X., Zhang, J., & Yu, Z. (2024) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3640457.3688108 · Cited by 24
The ethical evaluation of large language models and its optimization
Lyu, Y., & Du, Y. (2025) · AI and Ethics · doi:10.1007/s43681-024-00654-9 · Cited by 11
Context Embeddings for Efficient Answer Generation in Retrieval-Augmented Generation
Rau, D., Wang, S., Déjean, H., Clinchant, S., & Kamps, J. (2025) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3701551.3703527 · Cited by 11
“This is particularly critical for QA applications where reasoning over context from multiple documents is necessary, such as in multi-doc QA tasks [7, 16, 35].”
InfluentialMahbub, M., Srinivasan, S., Begoli, E., & Peterson, G. D. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac508 · Cited by 11
Disentangled Retrieval and Reasoning for Implicit Question Answering
Liu, Q., Geng, X., Wang, Y., Cambria, E., & Jiang, D. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3220933 · Cited by 11
DuReader-Retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine
Qiu, Y., Li, H., Qu, Y., Chen, Y., She, Q., Liu, J., Wu, H. Y., & Wang, H. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.357 · Cited by 16
TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack
Cao, Y., Li, D., Fang, M., Zhou, T., Gao, J., Zhan, Y., & Tao, D. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.821 · Cited by 15
Closed-book Question Generation via Contrastive Learning
Dong, X., Lu, J., Wang, J., & Caverlee, J. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.230 · Cited by 11
MemoReader: Large-Scale Reading Comprehension through Neural Memory Controller
Back, S., Yu, S., Indurthi, S. R., Kim, J., & Choo, J. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1237 · Cited by 12
Maharana, A., & Bansal, M. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.333 · Cited by 10
Farooq, H., & Kaushik, B. (2020) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs48265.2020.9121015 · Cited by 11
Improving QA Generalization by Concurrent Modeling of Multiple Biases
Wu, M., Moosavi, N. S., Rücklé, A., & Gurevych, I. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.74 · Cited by 14
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation
Liu, J., Shou, L., Pei, J., Gong, M., Yang, M., & Jiang, D. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.244 · Cited by 14
CoMSum and SIBERT: A Dataset and Neural Model for Query-Based Multi-document Summarization
Kulkarni, S., Chammas, S., Zhu, W., Sha, F., & Ie, E. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86331-9_6 · Cited by 11
I Know What You Want: Semantic Learning for Text ComprehensionPreprint
Zhang, Z., Wu, Y., Li, Z., He, S., Zhao, H., Zhou, X., & Zhou, X. (2018) · arXiv (Cornell University) · Cited by 12
Project PIAF: Building a Native French Question-Answering DatasetPreprint
Keraron, R., Lancrenon, G., Bras, M., Allary, F., Moyse, G., Scialom, T., Soriano-Morales, E.-P., & Staiano, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.00968 · Cited by 15
Studying Strategically: Learning to Mask for Closed-book QAPreprint
Ye, Q., Li, B. Z., Wang, S., Bolte, B., Ma, H., Yih, W.-T., Ren, X., & Khabsa, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.15856 · Cited by 15
ERNIE-Doc: A Retrospective Long-Document Modeling TransformerPreprint
Ding, S., Shang, J., Wang, S., Sun, Y., Tian, H., Wu, H. R., & Wang, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.15688 · Cited by 11
Bhakthavatsalam, S., Khashabi, D., Khot, T., Mishra, B. D., Richardson, K., Sabharwal, A., Schoenick, C., Tafjord, O., & Clark, P. E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.03315 · Cited by 11
Attention-guided Generative Models for Extractive Question AnsweringPreprint
Xu, P., Liang, D., Huang, Z., & Xiang, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.06393 · Cited by 22
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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