The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
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].”
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].”
From Harnessing the power of llms in practice: a survey on chatGPT and beyond (2024) · cites this paper as [56]
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Cites
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SQuAD: 100,000+ Questions for Machine Comprehension of Text
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Teaching Machines to Read and ComprehendPreprint
Hermann, K. M., Kočiský, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., & Blunsom, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.03340 · Cited by 2,855
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Bidirectional Attention Flow for Machine ComprehensionPreprint
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Semantic Parsing on Freebase from Question-Answer Pairs
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Building Watson: An Overview of the DeepQA Project
Ferrucci, D. A., Brown, E. W., Chu‐Carroll, J., Fan, J., Gondek, D. C., Kalyanpur, A. A., Lally, A., Murdock, J. W., Nyberg, E., Prager, J. M., et al. (2010) · AI Magazine · doi:10.1609/aimag.v31i3.2303 · Cited by 1,523
Modeling Relations and Their Mentions without Labeled Text
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Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations
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Self-citation
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
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Large Language Models Encode Clinical Knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2212.13138 · Cited by 4,083
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
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