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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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Барабаш, О., Vorvul, D., Musienko, A. P., & БЕЗСМЕРТНИЙ, В. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-89296-7_27 · Cited by 1
Wei, Y., & Li, L. (2024) · International Conference on Information Science and Education (ICISE-IE) · doi:10.1109/icise-ie64355.2024.11025434
Strengths-Leverage Chain-of-Thought: Enhancing Multimodal Reasoning with LLM and LMM
Xu, Q., & Zhang, L. (2024) · International Conference on Virtual Reality and Visualization · doi:10.1109/icvrv62410.2024.00063
A Benchmark Suite of Japanese Natural Questions
Uematsu, T., Wang, H., Fukuda, S., Kawahara, D., & Shibata, T. (2025) · Journal of Natural Language Processing · doi:10.5715/jnlp.32.497
Enabling retrieval-augmented generation and knowledge graphs with discourse analysis
Galitsky, B. A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-30046-2.00003-x
Rethinking Data Use in Large Language Models
Min, S., Hajishirzi, H., & Zettlemoyer, L. (2025) · Computational Linguistics · doi:10.1162/coli.a.573
Self-citationRefKG: A Knowledge-Driven System for Fact-Checking and Query Resolution Using Knowledge Graphs
Kumar, R. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-7760-3_28
Enhanced Multimodal Chain-of-Thought with Visual Self-Contrastive Distillation
Zheng, G., Kong, J., Wang, J., & Zhang, X. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209812
Mitigating Retrieval Errors in RAG Pipelines Through Ambiguity Detection and Interactive Feedback
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FINE: LLM Prompt Tuning Fused with Internal and External Knowledge for EAE
Guo, X., Zhang, L., Chen, Q., Wang, S., Liao, J., & Zheng, J. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229421
Boosting LLM Performance on Boolean Questions with Cluster-Based Embeddings
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DATA SCIENCE FOR MEASURING VISUAL INFLUENCE IN ART
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DATA DRIVEN PATRON ENGAGEMENT IN ART INSTITUTIONS
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Modular Arabic VQA System Using Pre-Trained Models
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Harvesting commonsense and hidden knowledge from web services
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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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