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Numbers are crucial for various real-world domains such as finance, economics, and science.
Is 27 a big number? correlational and causal connections among numerical categorization, number line estimation, and numerical magnitude comparison
Elida V. Laski and Robert S. Siegler. 2007 · 2007
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Number games, magnitude representation, and basic number skills in preschoolers
Jemma Catherine Whyte and Rebecca Bull. 2008 · 2008
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Children’s counting and concepts of number
Karen C Fuson. 2012 · 2012
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The approximate number system and its relation to early math achievement: Evidence from the preschool years
Justin W. Bonny and Stella F. Lourenco. 2013 · 2013
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How well do computers solve math word problems? large-scale dataset construction and evaluation
Danqing Huang, Shuming Shi, Chin-Yew Lin, Jian Yin, and Wei-Ying Ma. 2016 · 2016
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Annotating derivations: A new evaluation strategy and dataset for algebra word problems
Shyam Upadhyay and Ming-Wei Chang. 2017 · 2017
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Transforming question answering datasets into natural language inference datasets
Dorottya Demszky, Kelvin Guu, and Percy Liang. 2018 · 2018
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An introduction to the approximate number system
Darko Odic and Ariel Starr. 2018 · 2018
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MathQA: Towards interpretable math word problem solving with operation-based formalisms
Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi. 2019 · 2019
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Understanding Negative Numbers , pages 251–277. Springer International Publishing, Cham
Laura Bofferding. 2019 · 2019
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DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 2019
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Exploring numeracy in word embeddings
Aakanksha Naik, Abhilasha Ravichander, Carolyn Rose, and Eduard Hovy. 2019 · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
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Do NLP models know numbers? probing numeracy in embeddings
Eric Wallace, Yizhong Wang, Sujian Li, Sameer Singh, and Matt Gardner. 2019 · 2019
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Climbing towards NLU: On meaning, form, and understanding in the age of data
Emily M. Bender and Alexander Koller. 2020 · 2020
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Tabfact: A large-scale dataset for table-based fact verification
Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, and William Yang Wang. 2020 · 2020
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Understanding tables with intermediate pre-training
Julian Eisenschlos, Syrine Krichene, and Thomas Müller. 2020 · 2020
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Injecting numerical reasoning skills into language models
Mor Geva, Ankit Gupta, and Jonathan Berant. 2020 · 2020
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INFOTABS: Inference on tables as semi-structured data
Vivek Gupta, Maitrey Mehta, Pegah Nokhiz, and Vivek Srikumar. 2020 · 2020
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TaPas: Weakly supervised table parsing via pre-training
Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, Francesco Piccinno, and Julian Eisenschlos. 2020 · 2020
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-Trained Language Models
Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, and Xiang Ren. 2020 · 2020
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ToTTo: A controlled table-to-text generation dataset
Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, and Dipanjan Das. 2020 · 2020
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TaBERT: Pretraining for joint understanding of textual and tabular data
Pengcheng Yin, Graham Neubig, Wen-tau Yih, and Sebastian Riedel. 2020 · 2020
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Do language embeddings capture scales?
Xikun Zhang, Deepak Ramachandran, Ian Tenney, Yanai Elazar, and Dan Roth. 2020 · 2020
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Temporal common sense acquisition with minimal supervision
Ben Zhou, Qiang Ning, Daniel Khashabi, and Dan Roth. 2020 · 2020
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Extracting training data from large language models
MWP-BERT: Numeracy-augmented pre-training for math word problem solving
Zhenwen Liang, Jipeng Zhang, Lei Wang, Wei Qin, Yunshi Lan, Jie Shao, and Xiangliang Zhang. 2022 · 2022
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TAPEX: table pre-training via learning a neural SQL executor
Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, and Jian-Guang Lou. 2022 · 2022
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FiNER: Financial numeric entity recognition for XBRL tagging
Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos, and Georgios Paliouras. 2022 · 2022
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Evaluating transformer language models on arithmetic operations using number decomposition
Matteo Muffo, Aldo Cocco, and Enrico Bertino. 2022 · 2022
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Do language models understand measurements?
Sungjin Park, Seungwoo Ryu, and Edward Choi. 2022 · 2022
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Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel. 2021 · 2021
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Deberta: decoding-enhanced bert with disentangled attention
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2021 · 2021
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Numgpt: Improving numeracy ability of generative pre-trained models
Zhihua Jin, Xin Jiang, Xingbo Wang, Qun Liu, Yong Wang, Xiaozhe Ren, and Huamin Qu. 2021 · 2021
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Have you seen that number? investigating extrapolation in question answering models
Jeonghwan Kim, Giwon Hong, Kyung-min Kim, Junmo Kang, and Sung-Hyon Myaeng. 2021 · 2021
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Investigating numeracy learning ability of a text-to-text transfer model
Kuntal Kumar Pal and Chitta Baral. 2021 · 2021
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Numeracy enhances the literacy of language models
Avijit Thawani, Jay Pujara, and Filip Ilievski. 2021a · 2021
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Representing numbers in NLP: a survey and a vision
Avijit Thawani, Jay Pujara, Filip Ilievski, and Pedro Szekely. 2021b · 2021
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Yasaman Razeghi, Robert L Logan IV, Matt Gardner, and Sameer Singh. 2022 · 2022
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Enhancing tabular reasoning with pattern exploiting training
Abhilash Shankarampeta, Vivek Gupta, and Shuo Zhang. 2022 · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou. 2022 · 2022
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Towards robust numerical question answering: Diagnosing numerical capabilities of NLP systems
Jialiang Xu, Mengyu Zhou, Xinyi He, Shi Han, and Dongmei Zhang. 2022 · 2022
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ReasTAP: Injecting table reasoning skills during pre-training via synthetic reasoning examples
Yilun Zhao, Linyong Nan, Zhenting Qi, Rui Zhang, and Dragomir Radev. 2022 · 2022
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PURR: efficiently editing language model hallucinations by denoising language model corruptions
Anthony Chen, Panupong Pasupat, Sameer Singh, Hongrae Lee, and Kelvin Guu. 2023 · 2023
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Generic temporal reasoning with differential analysis and explanation
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Training data extraction from pre-trained language models: A survey
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Survey of hallucination in natural language generation
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Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning
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Augmented language models: a survey
Grégoire Mialon, Roberto Dessì, Maria Lomeli, Christoforos Nalmpantis, Ramakanth Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, Edouard Grave, Yann LeCun, and Thomas Scialom. 2023 · 2023
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Flan-moe: Scaling instruction-finetuned language models with sparse mixture of experts
Sheng Shen, Le Hou, Yanqi Zhou, Nan Du, Shayne Longpre, Jason Wei, Hyung Won Chung, Barret Zoph, William Fedus, Xinyun Chen, Tu Vu, Yuexin Wu, Wuyang Chen, Albert Webson, Yunxuan Li, Vincent Zhao, Hongkun Yu, Kurt Keutzer, Trevor Darrell, and Denny Zhou. 2023 · 2023
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Yunhu Ye, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, and Yongbin Li. 2023 · 2023
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Are NLP models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal. 2021 · 2094
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