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TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Open accessAbstract
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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Zhao, Z., Chen, L., Cao, R., Xu, H., Chen, X., & Yu, K. (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.132 · Cited by 5
Chen, Q., Teng, Y., Zhang, H., Jiang, K., Duan, Q., Li, X., Zhao, X., & Li, R. (2022) · International Conference on Computer and Communication Systems (ICCCS) · doi:10.1109/icccs55155.2022.9846198 · Cited by 6
Multi-Turn and Multi-Granularity Reader for Document-Level Event Extraction
Yang, H., Chen, Y., Liu, K., Zhao, J., Zhao, Z., & Sun, W. (2022) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3542925 · Cited by 4
MeetingQA: Extractive Question-Answering on Meeting Transcripts
Prasad, A., Bui, T., Yoon, S., Deilamsalehy, H., Dernoncourt, F., & Bansal, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.837 · Cited by 8
Enhancing text comprehension for Question Answering with Contrastive Learning
Lee, S., & Lee, M. (2023) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/2023.repl4nlp-1.7 · Cited by 4
Data-Efficient Auto-Regressive Document Retrieval for Fact Verification
Thorne, J. (2022) · Workshop on Simple and Efficient Natural Language Processing (SustaiNLP) · doi:10.18653/v1/2022.sustainlp-1.7 · Cited by 6
Lee, S., Park, E., Seo, D., Jeon, D., Kang, I.-H., & Na, S. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.177 · Cited by 4
Liu, J., Ren, J., Bai, R., Zhang, Z., & Lu, Z. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125701 · Cited by 4
Trippas, J. R. (2024) · ACM eBooks · doi:10.1145/3674127.3674135 · Cited by 5
SUGAR: Leveraging Contextual Confidence for Smarter Retrieval
Zubkova, H., Park, J.-H., & Lee, S.-W. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890064 · Cited by 9
On the Robustness of Generative Information Retrieval Models: An Out-of-Distribution Perspective
Liu, Y.-A., Zhang, R., Guo, J., Zhou, C., de Rijke, M., & Cheng, X. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88711-6_26 · Cited by 12
Key, Value, Compress: A Systematic Exploration of KV Cache Compression Techniques
Javidnia, N., Rouhani, B. D., & Koushanfar, F. (2025) · Custom Integrated Circuits Conference · doi:10.1109/cicc63670.2025.10983416 · Cited by 7
“The models are evaluated on a diverse set of datasets, including ARC-C and ARC-E [27], BoolQ [28], Hellaswag [29], OBQA [30], PIQA [31], WinoGrande [32], SciQ [33], MMLU [34], RTE [35], and TriviaQA [36].”
Alleviating the Fear of Losing Alignment in LLM Fine-tuning
Yang, K., Tao, G., Chen, X., & Xu, J. L. (2025) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp61157.2025.00171 · Cited by 10
“Specifically, we measure the utility performance on 5 representative benchmark datasets, including PIQA [58], GSM8K [59], TriviaQA [60], HumanEval [61], and MMLU [62].”
Multiple Perspective Answer Reranking for Multi-passage Reading Comprehension
Ren, M., Huang, H., Wei, R., Liu, H., Bai, Y., Wang, Y., & Gao, Y. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32236-6_67 · Cited by 4
Answer Generation through Unified Memories over Multiple Passages
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The Graph Reasoning Approach Based on the Dynamic Knowledge Auxiliary for Complex Fact Verification
Wang, Y., Xia, C., Si, C., Zhang, C., & Wang, T. (2020) · Electronics · doi:10.3390/electronics9091472 · Cited by 4
Arabic Reading Comprehension Benchmarks Created Semiautomatically
Biltawi, M. M., Awajan, A., & Tedmori, S. (2020) · International Arab Conference on Information Technology (ACIT) · doi:10.1109/acit50332.2020.9300111 · Cited by 4
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Zhang, S., Zhang, X., Lau, J. H., Chan, J., & Paris, C. L. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-67664-3_34 · Cited by 6
Improving Low resource Reading Comprehension via Cross lingual Transposition Rethinking
Wu, G., Xu, B., Qin, Y., Wang, W., & Wang, G. (2021) · International Joint Conference on Knowledge Graphs (IJCKG) · doi:10.1145/3502223.3502234 · Cited by 6
Survey of Machine Reading Comprehension Models and its Evaluation Metrics
Therasa, M., & Mathivanan, G. (2022) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc53470.2022.9754070 · Cited by 4
GameOfThronesQA: Answer-Aware Question-Answer Pairs for TV Series
Lahiri, A. K., & Hu, Q. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-99739-7_21 · Cited by 4
Le, H. T.-T., Ho, V.-D., Nguyen, D.-V., & Nguyen, N. L.-T. (2022) · NAFOSTED Conference on Information and Computer Science (NICS) · doi:10.1109/nics56915.2022.10013390 · Cited by 4
When to Read Documents or QA History: On Unified and Selective Open-domain QA
Lee, K., Han, S.-E., Hwang, S.-W., & Lee, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.401 · Cited by 4
Robust Question Answering against Distribution Shifts with Test-Time Adaption: An Empirical Study
Ye, H., Ding, Y., Li, J., & Ng, H. T. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.460 · Cited by 4
Convolutional Neural Networks and Recurrent Neural Networks
Yan, W. Q. (2023) · Texts in computer science · doi:10.1007/978-981-99-4823-9_3 · Cited by 8
Investigating better context representations for generative question answering
Francis, S., & Moens, M. (2023) · Information Retrieval · doi:10.1007/s10791-023-09420-7 · Cited by 5
Cross-lingual Open-Retrieval Question Answering for African Languages
Ogundepo, O., Gwadabe, T., Rivera, C., Clark, J. R., Ruder, S., Adelani, D. I., Dossou, B. F. P., Diop, A., Sikasote, C., Hacheme, G., et al. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.997 · Cited by 7
Detrimental Contexts in Open-Domain Question Answering
Oh, P., & Thorne, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.776 · Cited by 7
HPE: Answering Complex Questions over Text by Hybrid Question Parsing and Execution
Ye, L., Yavuz, S., Meng, R., Radev, D., Xiong, C., Joty, S., & Zhou, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.293 · Cited by 8
KGQuiz: Evaluating the Generalization of Encoded Knowledge in Large Language Models
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Barz, M., & Sonntag, D. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-9323-9_34 · Cited by 4
Huang, X., Tang, S., Zhang, Y., & Wei, B. (2020) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1900571 · Cited by 4
Yiğit, G., & Amasyalı, M. F. (2021) · Bilişim Teknolojileri Dergisi · doi:10.17671/gazibtd.810362 · Cited by 4
Synthetic Target Domain Supervision for Open Retrieval QA
Reddy, R. G., Iyer, B., Sultan, M. A., Zhang, R., Sil, A., Castelli, V., Florian, R., & Roukos, S. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3463085 · Cited by 5
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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