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The `pre-train, prompt, predict' paradigm of large language models (LLMs) has achieved remarkable success in open-domain question answering (OD-QA).
Language models are few-shot learners
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Answering complex open-domain questions with multi-hop dense retrieval
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Legal problem question answer genre across jurisdictions and cultures
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Layoutlmv2: Multi-modal pre-training for visually-rich document understanding
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YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia
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Impression management in organizations: Critical questions, answers, and areas for future research
Bolino, M.; Long, D.; and Turnley, W. 2016 · 2016
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Reading wikipedia to answer open-domain questions
Chen, D.; Fisch, A.; Weston, J.; and Bordes, A. 2017 · 2017
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FEVER: a large-scale dataset for fact extraction and VERification
Thorne, J.; Vlachos, A.; Christodoulopoulos, C.; and Mittal, A. 2018 · 2018
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A data-integration analysis on road emissions and traffic patterns
Qu, A.; Wang, Y.; Hu, Y.; Wang, Y.; and Baroud, H. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; and Liu, P. J. 2020 · 2020
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Hierarchy-Aware Multi-Hop Question Answering over Knowledge Graphs
Dong, J.; Zhang, Q.; Huang, X.; Duan, K.; Tan, Q.; and Jiang, Z. 2023 · 2023
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Survey of hallucination in natural language generation
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
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A knowledge graph based question answering method for medical domain
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Docvqa: A dataset for vqa on document images
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Scaling instruction-finetuned language models
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Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias
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How well do Large Language Models perform in Arithmetic tasks?
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