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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Past benchmarks for information retrieval mostly focus on easily found information and have become saturated by recent language models.
“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
Section 7.2 identifies TriviaQA as a benchmark with examples usable for information retrieval, and the Introduction says earlier reading-comprehension datasets often reached near-human performance quickly. But the paper does not establish that most retrieval benchmarks concern easily found information or have been saturated by recent language models. Section 6.3 says TriviaQA itself was not solved by information retrieval and its tested models lagged far behind humans. (LLM, conf=0.83)
Experimental — accuracy not yet measuredTraditional benchmarks such as NaturalQuestions and TriviaQA primarily feature single-hop questions, which typically do not require multi-step reasoning or iterative retrieval.
“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
The Dataset Analysis (§4; page not supplied) finds that 40% of sampled TriviaQA question–evidence pairs required information scattered across multiple sentences, consistent with a majority not requiring that kind of reasoning. But the paper does not classify TriviaQA questions as single-hop or assess iterative retrieval, and it does not address NaturalQuestions. A single-sentence question (§7.2) is not necessarily a single-hop question. (LLM, conf=0.82)
Experimental — accuracy not yet measuredOn TriviaQA, Chinchilla substantially outperforms Gopher in both filtered and unfiltered sets.
“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
Imitative falsehoods are less likely to be covered by existing question-answering benchmarks.
“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
The Introduction compares TriviaQA with existing question-answering datasets, and §6.4 discusses errors such as paraphrasing and distractor entities. Neither addresses whether imitative falsehoods are less likely to be covered by those benchmarks; searches for that phrase and for false beliefs or misleading answers found no relevant passage. The supplied copy gives no page numbers. (LLM, conf=0.74)
Experimental — accuracy not yet measuredLLMs perform better on nearly all closed-book QA datasets, such as NaturalQuestions, WebQuestions, and TriviaQA.
“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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“schler et al., 2016), and CNN/DailyMail (Hermann et al., 2015), contained questions whose answers could be determined from surface-level cues alone (i.e., answers were “explicitly stated”). TriviaQA (Joshi et al., 2017) broadened this task by providing several articles with a question, and used questions authored inde…”
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BackgroundRetrieval Augmentation Reduces Hallucination in Conversation
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Event Extraction by Answering (Almost) Natural Questions
Du, X., & Cardie, C. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.49 · Cited by 437 · see citing works
A Robustly Optimized BERT Pre-training Approach with Post-training
Liu, Z., Lin, W., Shi, Y., & Zhao, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-84186-7_31 · Cited by 431 · see citing works
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment AnalysisPreprint
Xu, H., Liu, B., Shu, L., & Yu, P. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.02232 · Cited by 504 · see citing works
SciTaiL: A Textual Entailment Dataset from Science Question Answering
Khot, T., Sabharwal, A., & Clark, P. E. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12022 · Cited by 416 · see citing works
GPT-NeoX-20B: An Open-Source Autoregressive Language Model
Black, S., Biderman, S., Hallahan, E., Anthony, Q. G., Gao, L., Golding, L., He, H., Leahy, C., McDonell, K., Phang, J., et al. (2022) · doi:10.18653/v1/2022.bigscience-1.9 · Cited by 407 · see citing works
MLQA: Evaluating Cross-lingual Extractive Question Answering
Lewis, P., Oğuz, B., Rinott, R., Riedel, S., & Schwenk, H. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.653 · Cited by 424 · see citing works
Simple and Effective Multi-Paragraph Reading Comprehension
Clark, C., & Gardner, M. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1078 · Cited by 402 · see citing works
GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
Ainslie, J., Lee-Thorp, J., de Jong, M., Zemlyanskiy, Y., Lebron, F., & Sanghai, S. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.298 · Cited by 454 · see citing works
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
Dua, D., Wang, Y., Dasigi, P., Stanovsky, G., Singh, S., & Gardner, M. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1246 · Cited by 393 · see citing works
“…to the reading comprehension challenge, around tracking conversational state (Reddy et al., 2018; Choi et al., 2018), requiring passage retrieval (Joshi et al., 2017; Yang et al., 2018), mis-matched passages and questions (Saha et al., 2018; Kocisk´y et al., 2018; Rajpurkar et al., 2018),…”
BackgroundTraining a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., Henighan, T., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.05862 · Cited by 442 · see citing works
Don’t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases
Clark, C., Yatskar, M., & Zettlemoyer, L. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1418 · Cited by 381 · see citing works
“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 391 · see citing works
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 372 · see citing works
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 406 · see citing works
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 397 · see citing works
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 · see citing works
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 · see citing works
DocRED: A Large-Scale Document-Level Relation Extraction Dataset
Yao, Y., Ye, D., Li, P., Han, X., Lin, Y., Liu, Z., Liu, Z., Huang, L., Zhou, J., & Sun, M. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1074 · Cited by 550 · see citing works
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 432 · see citing works
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 · see citing works
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 374 · see citing works
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 · see citing works
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 · see citing works
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 333 · see citing works
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 308 · see citing works
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 296 · see citing works
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 346 · see citing works
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 · see citing works
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 245 · see citing works
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 · see citing works
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 246 · see citing works
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 · see citing works
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 · see citing works
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 227 · see citing works
Autoregressive Entity RetrievalPreprint
Cao, N. D., Izacard, G., Riedel, S., & Petroni, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.00904 · Cited by 307 · see citing works
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 220 · see citing works
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 224 · see citing works
What large language models know and what people think they know
Steyvers, M., Tejeda, H., Kumar, A., Belem, C., Karny, S., Hu, X., Mayer, L. W., & Smyth, P. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-024-00976-7 · Cited by 209 · see citing works
Gao, J., Galley, M., & Li, L. C. (2019) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000074 · Cited by 44 · see citing works
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 · see citing works
Self-citationHybridQA: 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 216 · see citing works
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 425 · see citing works
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 194 · see citing works
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 201 · see citing works
Mihaylov, T., & Frank, A. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1076 · Cited by 196 · see citing works
Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval
Gao, L., & Callan, J. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.203 · Cited by 251 · see citing works
Generative AI in Higher Education: The ChatGPT Effect
Chan, C. K. Y., & Colloton, T. (2024) · Routledge · doi:10.4324/9781003459026 · Cited by 203 · see citing works
Retrospective Reader for Machine Reading Comprehension
Zhang, Z., Yang, J., & Zhao, H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i16.17705 · Cited by 200 · see citing works
AmbigQA: Answering Ambiguous Open-domain Questions
Min, S., Michael, J., Hajishirzi, H., & Zettlemoyer, L. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.466 · Cited by 219 · see citing works
Self-citationEvent 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 186 · see citing works
Muppet: Massive Multi-task Representations with Pre-Finetuning
Aghajanyan, A., Gupta, A., Shrivastava, A., Chen, X., Zettlemoyer, L., & Gupta, S. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.468 · Cited by 183 · see citing works
Self-citationSPoT: Better Frozen Model Adaptation through Soft Prompt Transfer
Vu, T., Lester, B., Constant, N., Al‐Rfou, R., & Cer, D. M. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.346 · Cited by 204 · see citing works
Editing Factual Knowledge in Language Models
De Cao, N., Aziz, W., & Titov, I. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.522 · Cited by 180 · see citing works
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 · see citing works
“, 2019) and TriviaQA (Joshi et al., 2017), Chinchilla outperforms Gopher in all cases.”
InfluentialCoreferential Reasoning Learning for Language Representation
Ye, D., Lin, Y., Du, J., Liu, Z., Li, P., Sun, M., & Liu, Z. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.582 · Cited by 174 · see citing works
Harvesting Paragraph-level Question-Answer Pairs from Wikipedia
Du, X., & Cardie, C. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1177 · Cited by 168 · see citing works
Language Models (Mostly) Know What They Know
Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, D., Perez, E., Schiefer, N., Hatfield-Dodds, Z., DasSarma, N., Tran-Johnson, E., et al. (2022) · arXiv · doi:10.48550/arxiv.2207.05221 · Cited by 187 · see citing works
Symbolic Discovery of Optimization Algorithms
Chen, X., Liang, C., Huang, D., Real, E., Wang, K., Liu, Y., Pham, H., Dong, X., Luong, T., Hsieh, C.-J., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.06675 · Cited by 303 · see citing works
“• NLG: TriviaQA [45], Natural Questions [51], Web Questions [6].”
BackgroundCondenser: a Pre-training Architecture for Dense Retrieval
Gao, L., & Callan, J. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.75 · Cited by 199 · see citing works
Transformer models used for text-based question answering systems
Nassiri, K., & Akhloufi, M. A. (2022) · Applied Intelligence · doi:10.1007/s10489-022-04052-8 · Cited by 177 · see citing works
Conversational Agents: Goals, Technologies, Vision and Challenges
Allouch, M., Azaria, A., & Azoulay, R. (2021) · Sensors · doi:10.3390/s21248448 · Cited by 168 · see citing works
Evaluating Retrieval Quality in Retrieval-Augmented Generation
Salemi, A., & Zamani, H. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657957 · Cited by 192 · see citing works
- 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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