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Large language models (LLMs) have demonstrated impressive capabilities in general scenarios, exhibiting a level of aptitude that approaches, in some aspects even surpasses, human-level intelligence.
On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 1919
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A demonstration of the nonfeasibility of fully automatic high quality translation
Yehoshua Bar-Hillel. 1960 · 1960
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The future of mt is now and bar-hillel was (almost entirely) right
Elliott Macklovitch. 1995 · 1995
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Computer-aided translation technology: A practical introduction
Lynne Bowker. 2002 · 2002
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Translation: An advanced resource book
Basil Hatim and Jeremy Munday. 2004 · 2004
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Basic concepts and models for interpreter and translator training
Daniel Gile. 2009 · 2009
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A process study of computer-aided translation
Philipp Koehn. 2009 · 2009
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Multidimensional quality metrics: a flexible system for assessing translation quality
Aljoscha Burchardt. 2013 · 2013
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Exploring Translation Theories , 2 edition
Anthony. Pym. 2014 · 2014
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In other words: A coursebook on translation
Mona Baker. 2018 · 2018
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Controlled hallucinations: Learning to generate faithfully from noisy data
Katja Filippova. 2020 · 2020
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The box is in the pen: Evaluating commonsense reasoning in neural machine translation
Jie He, Tao Wang, Deyi Xiong, and Qun Liu. 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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BLEURT: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020 · 2020
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Experts, errors, and context: A large-scale study of human evaluation for machine translation
Markus Freitag, George Foster, David Grangier, Viresh Ratnakar, Qijun Tan, and Wolfgang Macherey. 2021 · 2021
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To ship or not to ship: An extensive evaluation of automatic metrics for machine translation
Tom Kocmi, Christian Federmann, Roman Grundkiewicz, Marcin Junczys-Dowmunt, Hitokazu Matsushita, and Arul Menezes. 2021 · 2021
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Detecting hallucinated content in conditional neural sequence generation
Chunting Zhou, Graham Neubig, Jiatao Gu, Mona Diab, Francisco Guzmán, Luke Zettlemoyer, and Marjan Ghazvininejad. 2021 · 2021
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Quality-aware decoding for neural machine translation
Patrick Fernandes, António Farinhas, Ricardo Rei, José G. C. de Souza, Perez Ogayo, Graham Neubig, and Andre Martins. 2022 · 2022
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Results of WMT22 metrics shared task: Stop using BLEU – neural metrics are better and more robust
Markus Freitag, Ricardo Rei, Nitika Mathur, Chi-kiu Lo, Craig Stewart, Eleftherios Avramidis, Tom Kocmi, George Foster, Alon Lavie, and André F. T. Martins. 2022 · 2022
Cited alongside, same era.
Bridging the data gap between training and inference for unsupervised neural machine translation
Zhiwei He, Xing Wang, Rui Wang, Shuming Shi, and Zhaopeng Tu. 2022 · 2022
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Hyuhng Joon Kim, Hyunsoo Cho, Junyeob Kim, Taeuk Kim, Kang Min Yoo, and Sang-goo Lee. 2022 · 2022
Cited alongside, same era.
Findings of the 2022 conference on machine translation (WMT22)
Tom Kocmi, Rachel Bawden, Ondřej Bojar, Anton Dvorkovich, Christian Federmann, Mark Fishel, Thamme Gowda, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Rebecca Knowles, Philipp Koehn, Christof Monz, Makoto Morishita, Masaaki Nagata, Toshiaki Nakazawa, Michal Novák, Martin Popel, and Maja Popović. 2022 · 2022
Cited alongside, same era.
Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023 · 2023
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Marzena Karpinska and Mohit Iyyer. 2023 · 2023
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang (Shane) Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2023 · 2023
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Chain-of-dictionary prompting elicits translation in large language models
Hongyuan Lu, Haoyang Huang, Dongdong Zhang, Haoran Yang, Wai Lam, and Furu Wei. 2023 · 2023
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New trends in machine translation using large language models: Case examples with chatgpt
Chenyang Lyu, Jitao Xu, and Longyue Wang. 2023 · 2023
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Junlong Li, Zhuosheng Zhang, and Hai Zhao. 2022 · 2022
Cited alongside, same era.
Holistic evaluation of language models
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al. 2022 · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
Cited alongside, same era.
COMET-22: Unbabel-IST 2022 submission for the metrics shared task
Ricardo Rei, José G. C. de Souza, Duarte Alves, Chrysoula Zerva, Ana C Farinha, Taisiya Glushkova, Alon Lavie, Luisa Coheur, and André F. T. Martins. 2022a · 2022
Cited alongside, same era.
CometKiwi: IST-unbabel 2022 submission for the quality estimation shared task
Ricardo Rei, Marcos Treviso, Nuno M. Guerreiro, Chrysoula Zerva, Ana C Farinha, Christine Maroti, José G. C. de Souza, Taisiya Glushkova, Duarte Alves, Luisa Coheur, Alon Lavie, and André F. T. Martins. 2022b · 2022
Cited alongside, same era.
Prompting palm for translation: Assessing strategies and performance
David Vilar, Markus Freitag, Colin Cherry, Jiaming Luo, Viresh Ratnakar, and George Foster. 2022 · 2022
Cited alongside, same era.
In-context examples selection for machine translation
Sweta Agrawal, Chunting Zhou, Mike Lewis, Luke Zettlemoyer, and Marjan Ghazvininejad. 2023 · 2023
Cited alongside, same era.
Investigating the translation performance of a large multilingual language model: the case of bloom
Rachel Bawden and François Yvon. 2023 · 2023
Cited alongside, same era.
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Boosting theory-of-mind performance in large language models via prompting
Shima Rahimi Moghaddam and Christopher J Honey. 2023 · 2023
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Adaptive machine translation with large language models
Yasmin Moslem, Rejwanul Haque, and Andy Way. 2023 · 2023
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Generative agents: Interactive simulacra of human behavior
Joon Sung Park, Joseph C O’Brien, Carrie J Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. 2023 · 2023
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Towards making the most of chatgpt for machine translation
Keqin Peng, Liang Ding, Qihuang Zhong, Li Shen, Xuebo Liu, Min Zhang, Yuanxin Ouyang, and Dacheng Tao. 2023 · 2023
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Jonathan Pilault, Xavier Garcia, Arthur Bražinskas, and Orhan Firat. 2023 · 2023
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Sharegpt: Share your wildest chatgpt conversations with one click
ShareGPT. 2023 · 2023
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Large language models can be easily distracted by irrelevant context
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed Chi, Nathanael Schärli, and Denny Zhou. 2023 · 2023
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Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023 · 2023
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2023 · 2023
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Chatgpt or grammarly? evaluating chatgpt on grammatical error correction benchmark
Hao Wu, Wenxuan Wang, Yuxuan Wan, Wenxiang Jiao, and Michael R. Lyu. 2023 · 2023
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Large language models as optimizers
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V Le, Denny Zhou, and Xinyun Chen. 2023 · 2023
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Generate rather than retrieve: Large language models are strong context generators
Wenhao Yu, Dan Iter, Shuohang Wang, Yichong Xu, Mingxuan Ju, Soumya Sanyal, Chenguang Zhu, Michael Zeng, and Meng Jiang. 2023 · 2023
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