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
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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
Experimental — accuracy not yet measured
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208
Hierarchical Attention Networks for Document Classification
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A. J., & Hovy, E. H. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1174 · Cited by 5,022
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
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
Bajaj, P., Campos, D., Craswell, N., Deng, L., Gao, J., Liu, X., Majumder, R., McNamara, A., Mitra, B., Nguyen, T., et al. (2016) · arXiv · doi:10.48550/arxiv.1611.09268 · Cited by 2,473
Bidirectional Attention Flow for Machine ComprehensionPreprint
Seo, M. J., Kembhavi, A., Farhadi, A., & Hajishirzi, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01603 · Cited by 1,698
Semantic Parsing on Freebase from Question-Answer Pairs
Berant, J., Chou, A., Frostig, R., & Liang, P. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1160 · Cited by 1,593
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
Riedel, S., Yao, L., & McCallum, A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15939-8_10 · Cited by 1,333
Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations
Hoffmann, R., Zhang, C., Ling, X., Zettlemoyer, L., & Weld, D. S. (2011) · Cited by 878
Self-citation
Research works printed but not linked: 7
- Jordan Boyd-Graber, Brianna Satinoff, He He, and Hal Daumé III. 2012. Besting the quiz master: Crowdsourcing incremental classification games. In Proceedings of the 2012 Joint Con- ference on Empirical Methods in Natural Lan- guage Processing and Computational Natural Lan- guage Learning. Association for Computational Linguistics, Jeju Island, Korea, pages 1290-1301. http://www.aclweb.org/anthology/D12-1118.
- Danqi Chen, Jason Bolton, and Christopher D. Man- ning. 2016. A thorough examination of the cnn/daily mail reading comprehension task. In Pro- ceedings of the 54th Annual Meeting of the As- sociation for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Berlin, Germany, pages 2358-2367. http://www.aclweb.org/anthology/P16-1223.
- Takeshi Onishi, Hai Wang, Mohit Bansal, Kevin Gim- pel, and David McAllester. 2016. Who did what: A large-scale person-centered cloze dataset. In Pro- ceedings of the 2016 Conference on Empirical Meth- ods in Natural Language Processing. Association for Computational Linguistics, Austin, Texas, pages 2230-2235. https://aclweb.org/anthology/D16- 1241.
- Denis Paperno, Germán Kruszewski, Angeliki Lazari- dou, Ngoc Quan Pham, Raffaella Bernardi, San- dro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernandez. 2016. The lambada dataset: Word prediction requiring a broad discourse con- text. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Vol- ume 1: Long Papers). Association for Computa- tional Linguistics, Berlin, Germany, pages 1525- 1534. http://www.aclweb.org/anthology/P16-1144.
- Panupong Pasupat and Percy Liang. 2015. Com- positional semantic parsing on semi-structured ta- bles. In Proceedings of the 53rd Annual Meet- ing of the Association for Computational Lin- guistics and the 7th International Joint Confer- ence on Natural Language Processing of the Asian Federation of Natural Language Process- ing, ACL 2015, July 26-31, 2015, Beijing, China, Volume 1: Long Papers. pages 1470-1480. http://aclweb.org/anthology/P/P15/P15-1142.pdf.
- Hai Wang, Mohit Bansal, Kevin Gimpel, and David McAllester. 2015. Machine comprehension with syntax, frames, and semantics. In Proceedings of the 53rd Annual Meeting of the Association for Compu- tational Linguistics and the 7th International Joint Conference on Natural Language Processing (Vol- ume 2: Short Papers). Association for Computa- tional Linguistics, Beijing, China, pages 700-706. http://www.aclweb.org/anthology/P15-2115.
- Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. Hierar- chical attention networks for document classifica- tion. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Lin- guistics, San Diego, California, pages 1480-1489. http://www.aclweb.org/anthology/N16-1174.
Cited by
Sorted by citations
Items 801–900 of 1,088
A Comprehensive Evaluation of Embedding Models and LLMs for IR and QA Across English and Italian
Oro, E., Granata, F. M., & Ruffolo, M. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9050141 · Cited by 12
“In question answering, two popular benchmark datasets that use F1 as one of the metrics for evaluation are SQuAD [43] and TriviaQA [71].”
Robust Neural Information Retrieval: An Adversarial and Out-of-Distribution Perspective
Liu, Y.-A., Zhang, R., Guo, J., de Rijke, M., Fan, Y., & Cheng, X. (2025) · ACM Transactions on Information Systems · doi:10.1145/3768153 · Cited by 25
Lilian, J. F., Sundarakantham, K., & Shalinie, S. M. (2021) · Sadhana · doi:10.1007/s12046-021-01706-0 · Cited by 7
Keywords-aware dynamic graph neural network for multi-hop reading comprehension
Jia, M., Liao, L., Wang, W., Li, F., Chen, Z., Li, J., & Huang, H. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.05.110 · Cited by 8
Analyzing Multi-Task Learning for Abstractive Text Summarization
Kirstein, F., Wahle, J. P., Ruas, T., & Gipp, B. (2022) · Workshop on Generation, Evaluation and Metrics (GEM) · doi:10.18653/v1/2022.gem-1.5 · Cited by 9
SciPhyRAG - Retrieval Augmentation to Improve LLMs on Physics Q &A
Anand, A., Goel, A., Hira, M., Buldeo, S., Kumar, J., Verma, A., Gupta, R., & Shah, R. R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-49601-1_4 · Cited by 8
Gandalf: Learning Label-label Correlations in Extreme Multi-label Classification via Label Features
Kharbanda, S., Gupta, D., Schultheis, E., Banerjee, A., Hsieh, C., & Babbar, R. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3672063 · Cited by 7
Survey of Large Language Models for Answering Questions Across Various Fields
Shailendra, P., Ghosh, R. C., Kumar, R., & Sharma, N. (2024) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs60874.2024.10717078 · Cited by 12
Yang, Z., Xia, H., Li, J., Chen, Z., Zhu, Z., & Shen, W. (2025) · Electronics · doi:10.3390/electronics14030461 · Cited by 12
Understanding Knowledge Drift in LLMs Through Misinformation
Fastowski, A., & Kasneci, G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-82346-6_5 · Cited by 16
DIM Reader: Dual Interaction Model for Machine Comprehension
Liu, Z., Huang, D., Huang, K., & Zhang, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-69005-6_32 · Cited by 7
Can Machines Learn to Comprehend Scientific Literature?
Park, D., Choi, Y., Kim, D., Yu, M., Kim, S., & Kang, J. (2019) · IEEE Access · doi:10.1109/access.2019.2891666 · Cited by 7
Improved Machine Reading Comprehension Using Data Validation for Weakly Labeled Data
Yang, Y., Kang, S., & Seo, J. (2020) · IEEE Access · doi:10.1109/access.2019.2963569 · Cited by 7
Coarse and Fine Granularity Graph Reasoning for Interpretable Multi-Hop Question Answering
Zhang, M., Li, F., Wang, Y., Zhang, Z., Zhou, Y., & Li, X. (2020) · IEEE Access · doi:10.1109/access.2020.2981134 · Cited by 7
MTQA: Text‐Based Multitype Question and Answer Reading Comprehension Model
Chen, D., Ma, Z., Lin, W., Ma, J., & Zhu, Y. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/8810366 · Cited by 7
Perhaps PTLMs Should Go to School – A Task to Assess Open Book and Closed Book QA
Ciosici, M. R., Cecil, J., Lee, D., Hedges, A., Freedman, M., & Weischedel, R. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.493 · Cited by 7
Evidence Augment for Multiple-Choice Machine Reading Comprehension by Weak Supervision
Luo, D., Zhang, P., Ma, L., Zhu, X., Zhou, M., Liang, Q., Wang, B., & Wang, L. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86383-8_29 · Cited by 7
UKP-SQUARE: An Online Platform for Question Answering Research
Baumgärtner, T., Wang, K., Sachdeva, R. S., Geigle, G., Eichler, M., Poth, C., Sterz, H., Puerto, H., Ribeiro, L. F. R., Pfeiffer, J., et al. (2022) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.18653/v1/2022.acl-demo.2 · Cited by 7
Calibration of Machine Reading Systems at Scale
Dhuliawala, S., Adolphs, L., Das, R. K., & Sachan, M. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.133 · Cited by 9
An Encoder Attribution Analysis for Dense Passage Retriever in Open-Domain Question Answering
Li, M., Ma, X., & Lin, J. (2022) · Workshop on Trustworthy Natural Language Processing (TrustNLP) · doi:10.18653/v1/2022.trustnlp-1.1 · Cited by 7
Large-Scale Knowledge Synthesis and Complex Information Retrieval from Biomedical Documents
Saxena, S., Sangani, R., Prasad, S., Kumar, S., Athale, M., Awhad, R., & Vaddina, V. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020725 · Cited by 6
Yiğit, G., & Amasyalı, M. F. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-01918-2 · Cited by 7
FiD-ICL: A Fusion-in-Decoder Approach for Efficient In-Context Learning
Ye, Q., Beltagy, I., Peters, M. E., Ren, X., & Hajishirzi, H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.454 · Cited by 8
RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question Answering
Wang, C., Yu, H., & Zhang, Y. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.155 · Cited by 15
Embedding Recycling for Language Models
Saad-Falcon, J., Singh, A., Soldaini, L., D’Arcy, M., Cohan, A., & Downey, D. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.145 · Cited by 10
Alawwad, H., Alawwad, L., Alharbi, J., & Alharbi, A. (2023) · Proceedings of ArabicNLP 2023 · doi:10.18653/v1/2023.arabicnlp-1.77 · Cited by 7
Universal Self-Adaptive Prompting
Wan, X., Sun, R., Nakhost, H., Dai, H., Eisenschlos, J. M., Arık, S. Ö., & Pfister, T. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.461 · Cited by 21
Towards Inference-Oriented Reading Comprehension: ParallelQA
Wadhwa, S., Embar, V., Grabmair, M., & Nyberg, E. (2018) · doi:10.18653/v1/w18-1001 · Cited by 7
A Hybrid Network Model for Tibetan Question Answering
Sun, Y., & Xia, T. (2019) · IEEE Access · doi:10.1109/access.2019.2911320 · Cited by 8
Kamath, S., Grau, B., & Ma, Y. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-43887-6_58 · Cited by 7
LiveQA: A Question Answering Dataset Over Sports Live
Liu, Q., Jiang, S., Wang, Y., & Li, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-63031-7_23 · Cited by 8
An empirical analysis of existing systems and datasets toward general simple question answering
Han, N., Topić, G., Noji, H., Takamura, H., & Miyao, Y. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.465 · Cited by 7
Designing a Minimal Retrieve-and-Read System for Open-Domain Question Answering
Yang, S., & Seo, M. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.468 · Cited by 8
Interpretable Proof Generation via Iterative Backward Reasoning
Qu, H., Cao, Y. H., Gao, J., Ding, L., & Xu, R. (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.216 · Cited by 7
Ask Me Anything in Your Native Language
Sorokin, N., Abulkhanov, D., Piontkovskaya, I., & Malykh, V. (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.30 · Cited by 8
A Smart System to Generate and Validate Question Answer Pairs for COVID-19 Literature
Bhambhoria, R. V., Feng, L. Y., Sepehr, D., Chen, J., Cowling, C., Koçak, S. A., & Dolatabadi, E. (2020) · Workshop on Scholarly Document Processing (SDP) · doi:10.18653/v1/2020.sdp-1.4 · Cited by 7
A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete ReasoningPreprint
Hu, M., Peng, Y., Huang, Z., & Li, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.05514 · Cited by 7
Interactive Language Learning by Question AnsweringPreprint
Yuan, X., Côté, M.-A., Fu, J., Lin, Z., Pal, C., Bengio, Y., & Trischler, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.10909 · Cited by 13
Revealing the Importance of Semantic Retrieval for Machine Reading at ScalePreprint
Nie, Y., Wang, S., & Bansal, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.08041 · Cited by 16
Towards Robust Neural Retrieval Models with Synthetic Pre-TrainingPreprint
Reddy, R. G., Yadav, V., Sultan, M. A., Franz, M., Castelli, V., Ji, H., & Sil, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.07800 · Cited by 18
FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation
Vu, T., Iyyer, M., Wang, X., Constant, N., Wei, J., Wei, J., Tar, C., Sung, Y.-H., Zhou, D., Le, Q., et al. (2023) · arXiv · Cited by 7
Prompting with Pseudo-Code Instructions
Mishra, M., Kumar, P., Bhat, R. A., Murthy, R., Contractor, D., & Tamilselvam, S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.939 · Cited by 18
TriviaHG: A Dataset for Automatic Hint Generation from Factoid Questions
Mozafari, J., Jangra, A., & Jatowt, A. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657855 · Cited by 10
List-aware Reranking-Truncation Joint Model for Search and Retrieval-augmented Generation
Xu, S., Pang, L., Xu, J., Shen, H., & Cheng, X. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645336 · Cited by 8
FAQ-Gen: An automated system to generate domain-specific FAQs to aid content comprehension
Kale, S., Khaire, G., & Patankar, J. (2024) · Journal of Computer-Assisted Linguistic Research · doi:10.4995/jclr.2024.21178 · Cited by 9
AquaPipe: A Quality-Aware Pipeline for Knowledge Retrieval and Large Language Models
Yu, R., Huang, W., Bai, S., Zhou, J. B., & Wu, F. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3709661 · Cited by 8
“• Test Task : We test the performance of the RAG system by four QA tasks, namely SQuAD[49], WQ[10], TQA[34], NQ[39].”
Constructing and Evaluating Declarative RAG Pipelines in PyTerrier
Macdonald, C., Fang, J., Parry, A., & Meng, Z. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730150 · Cited by 9
Sun, K., Zhao, Z., Yang, H., Zhang, J., & Huang, G. Q. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3613372 · Cited by 6
Answering Open-Domain Multi-Answer Questions via a Recall-then-Verify Framework
Shao, Z., & Huang, M. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.128 · Cited by 6
A Feedback-Enhanced Two-Stage Framework for judicial machine reading comprehension
Lin, Z., Yang, F., Wu, X., Su, J., & Wang, X. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106178 · Cited by 6
Exploring The Landscape of Distributional Robustness for Question Answering Models
Awadalla, A., Wortsman, M., Ilharco, G., Min, S., Magnusson, I., Hajishirzi, H., & Schmidt, L. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.441 · Cited by 20
ArQuAD: An Expert-Annotated Arabic Machine Reading Comprehension Dataset
Obeidat, R., Al-Harbi, M., Al‐Ayyoub, M., & Alawneh, L. M. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10248-6 · Cited by 9
An evidence-based approach for open-domain question answering
Jafarzadeh, P., & Ensan, F. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02269-2 · Cited by 6
LoCo: Low-Bit Communication Adaptor for Large-Scale Model Training
Xie, X., Lin, Z., Toh, K.-C., & Zhou, P. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3544764 · Cited by 13
Packowski, S., Halilovic, I., Schlotfeldt, J., & Smith, T. (2024) · International Conference on Advances in Artificial Intelligence (ICAAI) · doi:10.1145/3704137.3704181 · Cited by 16
D’Oosterlinck, K., Xu, W., Develder, C., Demeester, T., Singh, A., Potts, C., Kiela, D., & Mehri, S. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00748 · Cited by 31
Navigating the Landscape of Hint Generation Research: From the Past to the Future
Jangra, A., Mozafari, J., Jatowt, A., & Muresan, S. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00751 · Cited by 15
Handling Anomalies of Synthetic Questions in Unsupervised Question Answering
Hong, G., Kang, J., Lim, D., & Myaeng, S.-H. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.306 · Cited by 7
Commonsense Knowledge Adversarial Dataset that Challenges ELECTRA
Lin, G., Miao, Y., Yang, X., Ou, W., Cui, L., Guo, W., & Miao, C. (2020) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv50220.2020.9305451 · Cited by 6
Evaluating Neural Model Robustness for Machine Comprehension
Wu, W. M. C., Arendt, D., & Volkova, S. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.210 · Cited by 6
Zhao, C., Xiong, C., Boyd-Graber, J. L., & Daumé, H. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.756 · Cited by 8
Li, R., Wang, L., Jiang, Z., Hu, Z., Zhao, M., & Lu, X. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07072-0 · Cited by 6
TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval
Leszczynski, M., Fu, D. Y., Chen, M. F., & Ré, C. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.169 · Cited by 9
Topic-aware multi-hop machine reading comprehension using weighted graphs
Mohammadi, A., Ramezani, R., & Baraani-Dastjerdi, A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119873 · Cited by 8
Multi-views contrastive learning for dense text retrieval
Yu, Y., Zeng, J., Zhong, L., Gao, M., Wen, J., & Wu, Y. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110624 · Cited by 6
Tourism Information QA Datasets for Smart Tourism Chatbot
Ko, T.-S., Jwa, M.-C., & Jwa, J.-W. (2023) · International Journal of Membrane Science and Technology · doi:10.15379/ijmst.v10i1.1451 · Cited by 6
CONSISTENT: Open-Ended Question Generation From News Articles
Chakrabarty, T., Lewis, J., & Muresan, S. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.517 · Cited by 6
Causal Intervention for Mitigating Name Bias in Machine Reading Comprehension
Zhu, J., Wu, S., Zhang, X., Hou, Y., & Feng, Z. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.812 · Cited by 6
Query Refinement Prompts for Closed-Book Long-Form QA
Amplayo, R. K., Webster, K., Collins, M., Das, D., & Narayan, S. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.444 · Cited by 23
The DistilBERT Model: A Promising Approach to Improve Machine Reading Comprehension Models
P, S. V., Shahapure, N. H., Rekha, P. M., Nethravathi, B., Khandelwal, P., Anand, A., Agrawal, P., & Srivastava, V. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i8.7957 · Cited by 9
Ranking and Sampling in Open-Domain Question Answering
Xu, Y., Lin, Z., Liu, Y., Liu, R., Wang, W., & Meng, D. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1245 · Cited by 6
Learn with Noisy Data via Unsupervised Loss Correction for Weakly Supervised Reading Comprehension
Zhang, X., Zhou, K., Wang, S., Zhang, F., Wang, Z., & Liu, J. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.236 · Cited by 6
Model Agnostic Answer Reranking System for Adversarial Question Answering
Majumder, S., Samant, C., & Durrett, G. (2021) · Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop (EACL SRW) · doi:10.18653/v1/2021.eacl-srw.8 · Cited by 6
Open-Domain Question Answering with Pre-Constructed Question Spaces
Xiao, J., Wang, L., Dernoncourt, F., Bui, T., Sun, T., & Han, J. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop (NAACL SRW) · doi:10.18653/v1/2021.naacl-srw.9 · Cited by 6
Does Structure Matter? Encoding Documents for Machine Reading Comprehension
Wan, H., Feng, S., Gunasekara, C., Patel, S. S., Joshi, S., & Lastras, L. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.367 · Cited by 6
Answering POI-recommendation Questions using Tourism Reviews
Contractor, D., Shah, K., Partap, A., Singla, P., & Mausam, M. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482320 · Cited by 7
Less Is More: Domain Adaptation with Lottery Ticket for Reading Comprehension
Zhu, H., Wang, Z., Zhang, H., Liu, M., Zhao, S., & Qin, B. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.95 · Cited by 7
QAInfomax: Learning Robust Question Answering System by Mutual Information MaximizationPreprint
Yeh, Y.-T., & Chen, Y.-N. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.00215 · Cited by 9
Multi-span Style Extraction for Generative Reading ComprehensionPreprint
Yang, J., Zhang, Z., & Zhao, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.07382 · Cited by 11
Towards an Atlas of Cultural Commonsense for Machine ReasoningPreprint
Acharya, A., Talamadupula, K., & Finlayson, M. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.05664 · Cited by 18
Grid Search Hyperparameter Benchmarking of BERT, ALBERT, and LongFormer on DuoRCPreprint
Quijano, A. J., Nguyen, S., & Ordoñez, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.06326 · Cited by 10
Complex QA and language models hybrid architectures, SurveyPreprint
Daull, X., Bellot, P., Bruno, E., Martin, V., & Murisasco, É. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2302.09051 · Cited by 32
A Question Answering Dataset for Temporal-Sensitive Retrieval-Augmented Generation
Chen, Z., Min, E., Zhao, X. H., Li, Y., Jia, X., Liao, J., Li, J., Wang, S., Hu, B., & Yin, D. (2025) · Scientific Data · doi:10.1038/s41597-025-06098-y · Cited by 12
“Early QA datasets, such as Natural Questions (NQ)[17], TriviaQA [14], and MS MARCO [15], primarily assess open-domain retrieval, relying on web documents and knowledge graphs.”
Lessons from the Trenches on Reproducible Evaluation of Language Models
Biderman, S., Schoelkopf, H., Sutawika, L., Gao, L., Tow, J., Abbasi, B., Aji, A. F., Ammanamanchi, P. S., Black, S., Clive, J., et al. (2024) · arXiv · doi:10.48550/arxiv.2405.14782 · Cited by 9
“Alternatively, when a reference answer is known, one can perform string-matching approaches heuristically to determine whether the model’s answer matches the ground truth (Dua et al., 2019; Joshi et al., 2017; Hendrycks et al., 2021).”
QUAREL: A Dataset and Models for Answering Questions about Qualitative Relationships
Tafjord, O., Clark, P. E., Gardner, M., Yih, W.-T., & Sabharwal, A. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33017063 · Cited by 10
(2025) · doi:10.1145/3833415 · Cited by 18
DANTE: Dialog graph enhanced prompt learning for conversational question answering over KGs
Li, J., Song, S., Yan, S., Hu, G., Lai, C., & Zhou, Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112294 · Cited by 5
Hermes: Algorithm-System Co-design for Efficient Retrieval-Augmented Generation At-Scale
Shen, M., Umar, M., Maeng, K., Suh, G. E., & Gupta, U. (2025) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3695053.3731076 · Cited by 8
“For retriever performance and accuracy analysis, we leverage queries from the TriviaQA-test dataset [18] and Natural Questions dataset [22].”
Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation
Wang, Y., Ren, R., Wang, Y., Zhao, W. X., Liu, J., Wu, H., & Wang, H. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730112 · Cited by 13
“We collect three extensively adopted open-domain QA benchmark datasets. including two single-hop QA datasets ( Natural Questions [16], TriviaQA [14]) and a multi-hop QA dataset ( Hot-potQA [41]).”
Enhancing long-form question answering via reflection with question decomposition
Xiao, J., Wu, W., Zhao, J., Fang, M., & Wang, J. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104274 · Cited by 6
Make Large Language Models Efficient: A Review
Mussa, A., Туймебаев, Ж. К., & Мансурова, М. (2025) · IEEE Access · doi:10.1109/access.2025.3605110 · Cited by 7
Efficient Tuning of Large Language Models for Knowledge-Grounded Dialogue Generation
Zhang, B., Ma, H., Li, D., Ding, J., Wang, J., Xu, B., & Lin, H. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.17 · Cited by 5
“We evaluate KEDiT on the following tasks included in KILT: FEVER (Thorne et al., 2018), AIDA CoNLL-YAGO (AY2; Hoffart et al., 2011), Zero Shot RE (Levy et al., 2017), Natural Questions (NQ; Kwiatkowski et al., 2019), HotpotQA (Yang et al., 2018), TriviaQA (Joshi et al., 2017), and ELI5 (Fan et al., 2019).”
TACR: A Table Alignment-based Cell Selection Method for HybridQA
Wu, J., Xu, Y., Gao, Y., Lou, J., Karlsson, B. F., & Okumura, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.409 · Cited by 8
Liu, J., Liu, J., Yang, Y., Wang, J., Wu, W., Zhao, D., & Yan, R. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.39 · Cited by 10
Retrieval-Augmented Knowledge-Intensive Dialogue
Wang, Z., Gong, P., Zhang, Y., Gu, J., & Yang, X. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44693-1_2 · Cited by 6
Nguyen, D.-H., Le, N.-K., & Nguyen, L.-M. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103514 · Cited by 6
Test-Time Self-Adaptive Small Language Models for Question Answering
Jeong, S., Baek, J., Cho, S., Hwang, S., & Park, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.1033 · Cited by 6
SpeechDPR: End-To-End Spoken Passage Retrieval For Open-Domain Spoken Question Answering
Lin, C.-J., Lin, G.-T., Chuang, Y.-S., Wu, W.-L., Li, S.-W. D., Mohamed, A., Lee, H.-Y., & Lee, L.-S. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10448210 · Cited by 5
Multi-Passage Machine Reading Comprehension Through Multi-Task Learning and Dual Verification
Li, X., Cheng, X., Xia, M., Ren, Q., He, Z., & Su, S. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3383103 · 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 ›
Are you an author, or do you see a problem with this page? Request a review
