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Table Question Answering (TQA) is an important but under-explored task.
Effective use of tables and figures in abstracts, presentations, and papers,
C. G. Durbin, · 2004
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Biological robustness,
H. Kitano, · 2004
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N. Rasul, M. Syed, Differential Diagnosis in Primary Care, Wiley, 2009. URL: https://books.google.com/books?id=r5cTAQAAMAAJ
2009
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Bioasq: A challenge on large-scale biomedical semantic indexing and question answering.,
G. Tsatsaronis, M. Schroeder, G. Paliouras, Y. Almirantis, I. Androutsopoulos, E. Gaussier, P. Gallinari, T. Artieres, M. R. Alvers, M. Zschunke, et al., · 2012
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Compositional semantic parsing on semi-structured tables,
P. Pasupat, P. Liang, · 2015
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Squad: 100,000+ questions for machine comprehension of text,
P. Rajpurkar, J. Zhang, K. Lopyrev, P. Liang, · 2016
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Tables as semi-structured knowledge for question answering,
S. K. Jauhar, P. Turney, E. Hovy, · 2016
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Search-based neural structured learning for sequential question answering,
M. Iyyer, W.-t. Yih, M.-W. Chang, · 2017
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Seq2sql: Generating structured queries from natural language using reinforcement learning,
V. Zhong, C. Xiong, R. Socher, · 2017
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Know what you don’t know: Unanswerable questions for squad,
P. Rajpurkar, R. Jia, P. Liang, · 2018
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Robust physical-world attacks on deep learning visual classification,
K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati, C. Xiao, A. Prakash, T. Kohno, D. Song, · 2018
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T. Yu, R. Zhang, K. Yang, M. Yasunaga, D. Wang, Z. Li, J. Ma, I. Li, Q. Yao, S. Roman, et al., · 2018
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The natural language decathlon: Multitask learning as question answering,
B. McCann, N. S. Keskar, C. Xiong, R. Socher, · 2018
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Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs,
D. Dua, Y. Wang, P. Dasigi, G. Stanovsky, S. Singh, M. Gardner, · 2019
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Reasoning over paragraph effects in situations,
K. Lin, O. Tafjord, P. Clark, M. Gardner, · 2019
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Tabfact: A large-scale dataset for table-based fact verification,
W. Chen, H. Wang, J. Chen, Y. Zhang, H. Wang, S. Li, X. Zhou, W. Y. Wang, · 2019
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Pubmedqa: A dataset for biomedical research question answering,
Q. Jin, B. Dhingra, Z. Liu, W. Cohen, X. Lu, · 2019
Cited alongside, same era.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter,
V. Sanh, L. Debut, J. Chaumond, T. Wolf, · 2019
Cited alongside, same era.
Mrqa 2019 shared task: Evaluating generalization in reading comprehension,
A. Fisch, A. Talmor, R. Jia, M. Seo, E. Choi, D. Chen, · 2019
Cited alongside, same era.
Open question answering over tables and text,
W. Chen, M.-W. Chang, E. Schlinger, W. Wang, W. W. Cohen, · 2020
Cited alongside, same era.
Adversarial filters of dataset biases,
R. Le Bras, S. Swayamdipta, C. Bhagavatula, R. Zellers, M. Peters, A. Sabharwal, Y. Choi, · 2020
Cited alongside, same era.
True few-shot learning with prompts–a real-world perspective,
T. Schick, H. Schütze, · 2021
Later among the works it cites.
Cross-task generalization via natural language crowdsourcing instructions,
S. Mishra, D. Khashabi, C. Baral, H. Hajishirzi, · 2022
Closest in time.
In-BoXBART: Get Instructions into Biomedical Multi-Task Learning,
M. Parmar, S. Mishra, M. Purohit, M. Luo, M. H. Murad, C. Baral, · 2022
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Improving biomedical information retrieval with neural retrievers (2022)
M. Luo, A. Mitra, T. Gokhale, C. Baral, · 2022
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Reframing instructional prompts to gptk’s language,
S. Mishra, D. Khashabi, C. Baral, Y. Choi, H. Hajishirzi, · 2022
Closest in time.
Training language models to follow instructions with human feedback,
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Image-based table recognition: data, model, and evaluation,
X. Zhong, E. ShafieiBavani, A. Jimeno Yepes, · 2020
Cited alongside, same era.
Biobert: a pre-trained biomedical language representation model for biomedical text mining,
J. Lee, W. Yoon, S. Kim, D. Kim, S. Kim, C. H. So, J. Kang, · 2020
Cited alongside, same era.
Transformers: State-of-the-art natural language processing,
T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer, P. von Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T. L. Scao, S. Gugger, M. Drame, Q. Lhoest, A. M. Rush, · 2020
Cited alongside, same era.
Finetuned language models are zero-shot learners,
J. Wei, M. Bosma, V. Y. Zhao, K. Guu, A. W. Yu, B. Lester, N. Du, A. M. Dai, Q. V. Le, · 2021
Cited alongside, same era.
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, G. Neubig, · 2021
Cited alongside, same era.
Multitask prompted training enables zero-shot task generalization,
V. Sanh, A. Webson, C. Raffel, S. H. Bach, L. Sutawika, Z. Alyafeai, A. Chaffin, A. Stiegler, T. L. Scao, A. Raja, et al., · 2021
Cited alongside, same era.
Metaicl: Learning to learn in context,
S. Min, M. Lewis, L. Zettlemoyer, H. Hajishirzi, · 2021
Cited alongside, same era.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al., · 2022
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Instructionner: A multi-task instruction-based generative framework for few-shot ner,
L. Wang, R. Li, Y. Yan, Y. Yan, S. Wang, W. Wu, W. Xu, · 2022
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How many data samples is an additional instruction worth?,
R. S. Puri, S. Mishra, M. Parmar, C. Baral, · 2022
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Grips: Gradient-free, edit-based instruction search for prompting large language models,
A. Prasad, P. Hase, X. Zhou, M. Bansal, · 2022
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Don’t blame the annotator: Bias already starts in the annotation instructions,
M. Parmar, S. Mishra, M. Geva, C. Baral, · 2022
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Promptchainer: Chaining large language model prompts through visual programming,
T. Wu, E. Jiang, A. Donsbach, J. Gray, A. Molina, M. Terry, C. J. Cai, · 2022
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Less is more: Summary of long instructions is better for program synthesis,
K. Kuznia, S. Mishra, M. Parmar, C. Baral, · 2022
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Benchmarking generalization via in-context instructions on 1,600+ language tasks,
Y. Wang, S. Mishra, P. Alipoormolabashi, Y. Kordi, A. Mirzaei, A. Arunkumar, A. Ashok, A. S. Dhanasekaran, A. Naik, D. Stap, et al., · 2022
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Generalized but not robust? comparing the effects of data modification methods on out-of-domain generalization and adversarial robustness,
T. Gokhale, S. Mishra, M. Luo, B. Sachdeva, C. Baral, · 2022
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Choose your qa model wisely: A systematic study of generative and extractive readers for question answering,
M. Luo, K. Hashimoto, S. Yavuz, Z. Liu, C. Baral, Y. Zhou, · 2022
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