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UNIFIEDQA: Crossing Format Boundaries with a Single QA System
Open accessAbstract
Question answering (QA) tasks have been posed using a variety of formats, such as extractive span selection, multiple choice, etc.This has led to format-specialized models, and even to an implicit division in the QA community.We argue that such boundaries are artificial and perhaps unnecessary, given the reasoning abilities we seek to teach are not governed by the format.As evidence, we use the latest advances in language modeling to build a single pre-trained QA model, UNIFIEDQA, that performs well across 20 QA datasets spanning 4 diverse formats.UNIFIEDQA performs on par with 8 different models that were trained on individual datasets themselves.Even when faced with 12 unseen datasets of observed formats, UNIFIEDQA performs surprisingly well, showing strong generalization from its out-offormat training data.Finally, fine-tuning this pre-trained QA model into specialized models results in a new state of the art on 10 factoid and commonsense QA datasets, establishing UNIFIEDQA as a strong starting point for building QA systems. 1 1 https://github.com/allenai/unifiedqaExtractive [SQuAD] Question: At what speed did the turbine operate?Context: (Nikola_Tesla) On his 50th birthday in 1906, Tesla demonstrated his 200 horsepower (150 kilowatts) 16,000 rpm bladeless turbine.... Gold answer: 16,000 rpm Multiple-Choice [ARC-challenge] Question: What does photosynthesis produce that helps plants grow?Candidate Answers: (A) water (B) oxygen (C) protein (D) sugar Gold answer: sugar Yes/No [BoolQ]Question: Was America the first country to have a president?Context: (President) The first usage of the word president to denote the highest official in a government was during the Commonwealth of England ... Gold answer: no Abstractive [NarrativeQA]Question: What does a drink from narcissus's spring cause the drinker to do? Context: Mercury has awakened Echo, who weeps for Narcissus, and states that a drink from Narcissus's spring causes the drinkers to "Grow dotingly enamored of themselves."... Gold answer: fall in love with themselves
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“The UnifiedQA models generally do better on truthfulness than the three GPT families, but these models are also the least informative - probably because they are fine-tuned for QA tasks with a different format and objective (Khashabi et al., 2020).”
From TruthfulQA: Measuring How Models Mimic Human Falsehoods (2022) · cites this paper as Khashabi et al., 2020
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“Longer phrasal or sentential answers are often used in language generation tasks [105] and in other tasks such as multiple-choice question answering (where the scores of multiple phrases are compared against each-other; Khashabi et al. [55]).”
From Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (2023) · cites this paper as [55]
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Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (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/n19-1423 · Cited by 54,325
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821
Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636
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
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., & Soricut, R. (2019) · arXiv · doi:10.48550/arxiv.1909.11942 · Cited by 4,308
Dense passage retrieval for open-domain question answering
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.550 · Cited by 3,575
Self-citationKnow What You Don’t Know: Unanswerable Questions for SQuAD
Rajpurkar, P., Jia, R., & Liang, P. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p18-2124 · Cited by 2,240
Natural Questions: A benchmark for question answering research
Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Devlin, J., Lee, K., et al. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00276 · Cited by 2,069
Research works printed but not linked: 3
- Dheeru Dua, Ananth Gottumukkala, Alon Talmor, Matt Gardner, and Sameer Singh. 2019a. Compre- hensive multi-dataset evaluation of reading compre- hension. In 2nd Workshop on Machine Reading for Question Answering.
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019b. Lan- guage models are unsupervised multitask learners. OpenAI Blog, 1(8):9.
- Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text trans- former. JMLR.
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Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions?
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Self-citationEnhancing Korean Named Entity Recognition With Linguistic Tokenization Strategies
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GNN is a Counter? Revisiting GNN for Question AnsweringPreprint
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GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation
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Self-citationMeta-path reasoning of knowledge graph for commonsense question answering
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Self-citationJointly Optimizing State Operation Prediction and Value Generation for Dialogue State TrackingPreprint
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Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension
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PUnifiedNER: A Prompting-Based Unified NER System for Diverse Datasets
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Reference Knowledgeable Network for Machine Reading Comprehension
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Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements
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Self-citation
Metadata
- Authors
- Khashabi, D., Min, S., Khot, T., Sabharwal, A., Tafjord, O., Clark, P., & Hajishirzi, H.
- Affiliations
- Allen Institute for Artificial Intelligence (US)
- University of Washington (US)
- Venue
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Published
- 2020
- Pages
- 1896-1907
- OpenAlex
- W3020908159
- Cited by
- 514
- References
- 51
- Citations by year
- 2026: 23 · 2025: 52 · 2024: 63 · 2023: 129 · 2022: 113 · 2021: 101 · 2020: 33
- FWCI
- 35.91
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Alfred P. Sloan Foundation
- Allen Institute for Artificial Intelligence
- Defense Advanced Research Projects Agency (N66001, N66001-19-2-403, N66001-19)
- Office of Naval Research (N00014-18, N00014-18-1-2826, ONR-N00014, N00014)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- question answering, pre-trained language models, multiple-choice question answering, yes/no question answering, fine-tuning, factoid question answering, commonsense question answering, SQuAD, ARC-Challenge
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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