Built on

Parallel iterative edit models for local sequence transduction
Original
Abhijeet Awasthi, Sunita Sarawagi, Rasna Goyal, Sabyasachi Ghosh, and Vihari Piratla. 2019 · 1910
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
Chinese spelling check evaluation at sighan bake-off 2013
Shih-Hung Wu, Chao-Lin Liu, and Lung-Hao Lee. 2013 · 2013
Earlier work this paper cites.
The conll-2014 shared task on grammatical error correction
Original
Hwee Tou Ng, Siew Mei Wu, Ted Briscoe, Christian Hadiwinoto, Raymond Hendy Susanto, and Christopher Bryant. 2014 · 2014
Earlier work this paper cites.
Introduction to sighan 2015 bake-off for chinese spelling check
Yuen-Hsien Tseng, Lung-Hao Lee, Li-Ping Chang, and Hsin-Hsi Chen. 2015 · 2015
Earlier work this paper cites.
A hybrid approach to automatic corpus generation for chinese spelling check
Dingmin Wang, Yan Song, Jing Li, Jialong Han, and Haisong Zhang. 2018 · 2018
Earlier work this paper cites.

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Rethinking the role of demonstrations: What makes in-context learning work?
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Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
Later among the works it cites.
Mathematical capabilities of chatgpt
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Simon Frieder, Luca Pinchetti, Ryan-Rhys Griffiths, Tommaso Salvatori, Thomas Lukasiewicz, Philipp Christian Petersen, Alexis Chevalier, and Julius Berner. 2023 · 2023
Closest in time.
Is chatgpt a good translator? a preliminary study
Original
Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, Xing Wang, and Zhaopeng Tu. 2023 · 2023
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Closest in time.

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2023

Does Correction Remain A Problem For Large Language Models?

Zhang, Xiaowu, Zhang, Xiaotian, Yang, Cheng et al.

Understand

As large language models, such as GPT, continue to advance the capabilities of natural language processing (NLP), the question arises: does the problem of correction still persist? This paper investigates the role of correction in the context of large language models by conducting two experiments.

The first experiment focuses on correction as a standalone task, employing few-shot learning techniques with GPT-like models for error correction.
The second experiment explores the notion of correction as a preparatory task for other NLP tasks, examining whether large language models can tolerate and perform adequately on texts containing certain levels of noise or errors.
By addressing these experiments, we aim to shed light on the significance of correction in the era of large language models and its implications for various NLP applications.

Built on

Parallel iterative edit models for local sequence transduction
Original
Abhijeet Awasthi, Sunita Sarawagi, Rasna Goyal, Sabyasachi Ghosh, and Vihari Piratla. 2019 · 1910
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
Chinese spelling check evaluation at sighan bake-off 2013
Shih-Hung Wu, Chao-Lin Liu, and Lung-Hao Lee. 2013 · 2013
Earlier work this paper cites.
The conll-2014 shared task on grammatical error correction
Original
Hwee Tou Ng, Siew Mei Wu, Ted Briscoe, Christian Hadiwinoto, Raymond Hendy Susanto, and Christopher Bryant. 2014 · 2014
Earlier work this paper cites.
Introduction to sighan 2015 bake-off for chinese spelling check
Yuen-Hsien Tseng, Lung-Hao Lee, Li-Ping Chang, and Hsin-Hsi Chen. 2015 · 2015
Earlier work this paper cites.
A hybrid approach to automatic corpus generation for chinese spelling check
Dingmin Wang, Yan Song, Jing Li, Jialong Han, and Haisong Zhang. 2018 · 2018
Earlier work this paper cites.

Similar

Tmu transformer system using bert for re-ranking at bea 2019 grammatical error correction on restricted track
Masahiro Kaneko, Kengo Hotate, Satoru Katsumata, and Mamoru Komachi. 2019 · 2019
Cited alongside, same era.
A simple recipe for multilingual grammatical error correction
Original
Sascha Rothe, Jonathan Mallinson, Eric Malmi, Sebastian Krause, and Aliaksei Severyn. 2021 · 2021
Cited alongside, same era.
Instantaneous grammatical error correction with shallow aggressive decoding
Original
Xin Sun, Tao Ge, Furu Wei, and Houfeng Wang. 2021 · 2021
Cited alongside, same era.
Read, listen, and see: Leveraging multimodal information helps chinese spell checking
Original
Heng-Da Xu, Zhongli Li, Qingyu Zhou, Chao Li, Zizhen Wang, Yunbo Cao, Heyan Huang, and Xian-Ling Mao. 2021 · 2021
Cited alongside, same era.
Palm: Scaling language modeling with pathways

Then

Rethinking the role of demonstrations: What makes in-context learning work?
Original
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
Later among the works it cites.
Mathematical capabilities of chatgpt
Original
Simon Frieder, Luca Pinchetti, Ryan-Rhys Griffiths, Tommaso Salvatori, Thomas Lukasiewicz, Philipp Christian Petersen, Alexis Chevalier, and Julius Berner. 2023 · 2023
Closest in time.
Is chatgpt a good translator? a preliminary study
Original
Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, Xing Wang, and Zhaopeng Tu. 2023 · 2023
Closest in time.
Chatgpt versus traditional question answering for knowledge graphs: Current status and future directions towards knowledge graph chatbots
Original
Reham Omar, Omij Mangukiya, Panos Kalnis, and Essam Mansour. 2023 · 2023
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022 · 2022

Cited alongside, same era.

Agieval: A human-centric benchmark for evaluating foundation models

Original

Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, and Nan Duan. 2023 · 2023

Closest in time.