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Spreadsheets are a vital tool for end-user data management.
The curious case of neural text degeneration
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A general language model for information retrieval
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Pre-training tasks for embedding-based large-scale retrieval
Chang, W.-C.; Yu, F. X.; Chang, Y.-W.; Yang, Y.; and Kumar, S. 2020 · 2002
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Dependence language model for information retrieval
Gao, J.; Nie, J.-Y.; Wu, G.; and Cao, G. 2004 · 2004
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Language model information retrieval with document expansion
Tao, T.; Wang, X.; Mei, Q.; and Zhai, C. 2006 · 2006
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GoalDebug: A spreadsheet debugger for end users
Abraham, R.; and Erwig, M. 2007 · 2007
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A proximity language model for information retrieval
Zhao, J.; and Yun, Y. 2009 · 2009
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Fuse: a reproducible, extendable, internet-scale corpus of spreadsheets
Barik, T.; Lubick, K.; Smith, J.; Slankas, J.; and Murphy-Hill, E. 2015 · 2015
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Enron’s spreadsheets and related emails: A dataset and analysis
Hermans, F.; and Murphy-Hill, E. 2015 · 2015
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Morgan Stanley Technology, Media & Telecom Conference
Morgan Stanley. 2015 · 2015
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Neural Machine Translation of Rare Words with Subword Units
Sennrich, R.; Haddow, B.; and Birch, A. 2016 · 2016
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Diverse beam search: Decoding diverse solutions from neural sequence models
Vijayakumar, A. K.; Cogswell, M.; Selvaraju, R. R.; Sun, Q.; Lee, S.; Crandall, D.; and Batra, D. 2016 · 2016
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Deepfix: Fixing common c language errors by deep learning
Gupta, R.; Pal, S.; Kanade, A.; and Shevade, S. 2017 · 2017
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Hierarchical neural story generation
Fan, A.; Lewis, M.; and Dauphin, Y. 2018 · 2018
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The adverse effects of code duplication in machine learning models of code
Allamanis, M. 2019 · 2019
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Parameter-efficient transfer learning for NLP
Houlsby, N.; Giurgiu, A.; Jastrzebski, S.; Morrone, B.; De Laroussilhe, Q.; Gesmundo, A.; Attariyan, M.; and Gelly, S. 2019 · 2019
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Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Reimers, N.; and Gurevych, I. 2019 · 2019
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CodeBERT: A Pre-Trained Model for Programming and Natural Languages
Feng, Z.; Guo, D.; Tang, D.; Duan, N.; Feng, X.; Gong, M.; Shou, L.; Qin, B.; Liu, T.; Jiang, D.; and Zhou, M. 2020 · 2020
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GraphCodeBERT: Pre-training Code Representations with Data Flow
Guo, D.; Ren, S.; Lu, S.; Feng, Z.; Tang, D.; Shujie, L.; Zhou, L.; Duan, N.; Svyatkovskiy, A.; Fu, S.; et al. 2020 · 2020
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BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Lewis, M.; Liu, Y.; Goyal, N.; Ghazvininejad, M.; Mohamed, A.; Levy, O.; Stoyanov, V.; and Zettlemoyer, L. 2020 · 2020
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Neurosymbolic Repair for Low-Code Formula Languages
Bavishi, R.; Joshi, H.; Cambronero, J.; Fariha, A.; Gulwani, S.; Le, V.; Radiček, I.; and Tiwari, A. 2022 · 2022
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Palm: Scaling language modeling with pathways
Chowdhery, A.; Narang, S.; Devlin, J.; Bosma, M.; Mishra, G.; Roberts, A.; Barham, P.; Chung, H. W.; Sutton, C.; Gehrmann, S.; et al. 2022 · 2022
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Table pre-training: A survey on model architectures, pre-training objectives, and downstream tasks
Dong, H.; Cheng, Z.; He, X.; Zhou, M.; Zhou, A.; Zhou, F.; Liu, A.; Han, S.; and Zhang, D. 2022 · 2022
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Incoder: A generative model for code infilling and synthesis
Fried, D.; Aghajanyan, A.; Lin, J.; Wang, S.; Wallace, E.; Shi, F.; Zhong, R.; Yih, W.-t.; Zettlemoyer, L.; and Lewis, M. 2022 · 2022
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Repair is nearly generation: Multilingual program repair with llms
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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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Benchmarking spreadsheet systems
Rahman, S.; Mack, K.; Bendre, M.; Zhang, R.; Karahalios, K.; and Parameswaran, A. 2020 · 2020
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Graph-based, self-supervised program repair from diagnostic feedback
Yasunaga, M.; and Liang, P. 2020 · 2020
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Tfix: Learning to fix coding errors with a text-to-text transformer
Berabi, B.; He, J.; Raychev, V.; and Vechev, M. 2021 · 2021
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Learning to complete code with sketches
Guo, D.; Svyatkovskiy, A.; Yin, J.; Duan, N.; Brockschmidt, M.; and Allamanis, M. 2021 · 2021
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Lora: Low-rank adaptation of large language models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2021 · 2021
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Deduplicating training data makes language models better
Lee, K.; Ippolito, D.; Nystrom, A.; Zhang, C.; Eck, D.; Callison-Burch, C.; and Carlini, N. 2021 · 2021
Cited alongside, same era.
Joshi, H.; Cambronero, J.; Gulwani, S.; Le, V.; Radicek, I.; and Verbruggen, G. 2022 · 2022
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Deduplicating training data mitigates privacy risks in language models
Kandpal, N.; Wallace, E.; and Raffel, C. 2022 · 2022
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Automating code review activities by large-scale pre-training
Li, Z.; Lu, S.; Guo, D.; Duan, N.; Jannu, S.; Jenks, G.; Majumder, D.; Green, J.; Svyatkovskiy, A.; Fu, S.; et al. 2022 · 2022
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CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
Nijkamp, E.; Pang, B.; Hayashi, H.; Tu, L.; Wang, H.; Zhou, Y.; Savarese, S.; and Xiong, C. 2022 · 2022
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Synchromesh: Reliable code generation from pre-trained language models
Poesia, G.; Polozov, O.; Le, V.; Tiwari, A.; Soares, G.; Meek, C.; and Gulwani, S. 2022 · 2022
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UL2: Unifying Language Learning Paradigms
Tay, Y.; Dehghani, M.; Tran, V. Q.; Garcia, X.; Wei, J.; Wang, X.; Chung, H. W.; Bahri, D.; Schuster, T.; Zheng, H. S.; Zhou, D.; Houlsby, N.; and Metzler, D. 2022 · 2022
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A systematic evaluation of large language models of code
Xu, F. F.; Alon, U.; Neubig, G.; and Hellendoorn, V. J. 2022 · 2022
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GitHub CoPilot
GitHub. 2021 · 2023
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HermEs: Interactive Spreadsheet Formula Prediction via Hierarchical Formulet Expansion
He, W.; Dong, H.; Gao, Y.; Fan, Z.; Guo, X.; Hou, Z.; Lv, X.; Jia, R.; Han, S.; and Zhang, D. 2023 · 2023
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FormaT5: Abstention and Examples for Conditional Table Formatting with Natural Language
Singh, M.; Cambronero, J.; Gulwani, S.; Le, V.; Negreanu, C.; Nouri, E.; Raza, M.; and Verbruggen, G. 2023 · 2023
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