Artificial intelligence literacy for the language industry – with particular emphasis on recent large language models such as GPT-4

References

Anderson, Philip W. (1972): More is different: Broken symmetry and the nature of hierarchical structure of science. Science 177(4047), 393–396. https://doi.org/10.1126/science.177.4047.393.10.1126/science.177.4047.393Search in Google Scholar

Angelone, Erik (2022): Weaving adaptive expertise into translator training. In: Massey, Gary/Huertas-Barros, Elsa/Katan, David (Eds.): The human translator in the 2020s. London: Routledge, 60–73. https://doi.org/10.4324/9781003223344.10.4324/9781003223344-5Search in Google Scholar

Angelone, Erik/Ehrensberger-Dow, Maureen/Massey, Gary (2019): Introduction. In: Angelone, Erik/Ehrensberger-Dow, Maureen/Massey, Gary (Eds.): The Bloomsbury companion to language industry studies. London: Bloomsbury Academic, 1–13. http://dx.doi.org/10.5040/9781350024960.0005.10.5040/9781350024960.0005Search in Google Scholar

Bach, Stephen/Sanh, Victor/Yong, Zheng Xin/Webson, Albert/Raffel, Colin/Nayak, Nihal V./Sharma, Abheesht/Kim, Taewoon/Bari, M Saiful/Fevry, Thibault/Alyafeai, Zaid/Dey, Manan/Santilli, Andrea/Sun, Zhiqing/Ben-david, Srulik/Xu, Canwen/Chhablani, Gunjan/Wang, Han/Fries, Jason/Al-shaibani, Maged/Sharma, Shanya/Thakker, Urmish/Almubarak, Khalid/Tang, Xiangru/Radev, Dragomir/Jiang, Mike Tian-jian/Rush, Alexander (2022):PromptSource: An integrated development environment and repository for natural language prompts. In: Basile, Valerio/Kozareva, Zornitsa/Stajner, Sanja (Eds.): Proceedings of the 60th annual meeting of the Association for Computational Linguistics: System demonstrations. Association for Computational Linguistics, 93–104. http://dx.doi.org/10.18653/v1/2022.acl-demo.9.10.18653/v1/2022.acl-demo.9Search in Google Scholar

Bahdanau, Dzmitry/Cho, Kyunghyun/Bengio, Yoshua (2014/2016): Neural machine translation by jointly learning to align and translate. In: Bengio, Yoshua/LeCun, Yann (Eds.): Proceedings of the 3rd international conference on learning representations (ICLR 2015), 1–15. Published on arXiv: https://arxiv.org/abs/1409.0473 (5 June 2023).Search in Google Scholar

Benaich, Nathan/Hogarth, Ian (2022): State of AI report 2022. https://www.stateof.ai/ (22 May 2023).Search in Google Scholar

Bender, Emily M./Gebru, Timnit/McMillan-Major, Angelina/Shmitchell, Shmargaret (2021): On the dangers of stochastic parrots: Can language models be too big? In: FAccT ’21: Proceedings of the 2021 ACM conference on fairness, accountability, and transparency. Association for Computing Machinery, 610–623. https://doi.org/10.1145/3442188.3445922.10.1145/3442188.3445922Search in Google Scholar

Bengio, Yoshua/Ducharme, Réjean/Vincent, Pascal/Jauvin, Christian (2003): A neural probabilistic language model. Journal of Machine Learning Research 3, 1137–1155. http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf (7 May 2023).Search in Google Scholar

Briakou, Eleftheria/Cherry, Colin/Foster, George (2023): Searching for needles in a haystack: On the role of incidental bilingualism in PaLM’s translation capability. arXiv. https://doi.org/10.48550/arXiv.2305.10266.10.18653/v1/2023.acl-long.524Search in Google Scholar

Bringsjord, Selmer/Govindarajulu, Naveen Sundar (2022): Artificial intelligence. In: Zalta, Edward N. (Ed.): The Stanford encyclopedia of philosophy (online edition). https://plato.stanford.edu/archives/fall2022/entries/artificial-intelligence/ (24 May 2023).Search in Google Scholar

Brown,Tom/Mann, Benjamin/Ryder, Nick/Subbiah, Melanie/Kaplan, Jared D./Dhariwal, Prafulla/Neelakantan, Arvind/Shyam, Pranav/Sastry, Girish/Askell, Amanda/Agarwal, Sandhini/Herbert-Voss, Ariel/Krueger, Gretchen/Henighan, Tom/Child, Rewon/Ramesh, Aditya/Ziegler, Daniel/Wu, Jeffrey/Winter, Clemens/Hesse, Chris/Chen, Mark/Sigler, Eric/Litwin, Mateusz/Gray, Scott/Chess, Benjamin/Clark, Jack/Berner, Christopher/McCandlish, Sam/Radford, Alec/Sutskever, Ilya/Amodei, Dario(2020):Languagemodelsarefew-shot learners. In: Larochelle, Hugo/Ranzato, Marc’Aurelio/Hadsell, Raja/Balcan, Maria-Florina/Lin, Hsuan-Tien (Eds.): Advances in neural information processing systems 33 (NeurIPS 2020), 1–25. https://papers.nips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html (7 May 2023).Search in Google Scholar

Bubeck, Sébastien/Chandrasekaran, Varun/Eldan, Ronen/Gehrke, Johannes/Horvitz, Eric/Kamar, Ece/Lee, Peter/Lee, Yin Tat/Li, Yuanzhi/Lundberg, Scott/Nori, Harsha/Palangi, Hamid/Tulio Ribeiro, Marco/Zhang, Yi (2023): Sparks of Artificial General Intelligence: Early experiments with GPT-4. arXiv. https://doi.org/10.48550/arXiv.2303.12712.Search in Google Scholar

Carl, Michael/Schaeffer, Moritz (2019): Outline of a relevance theoretical model of machine translation post-editing. In: Li, Defeng/Lei, Victoria/Lai, Cheng, He, Yuanjian (Eds.): Researching cognitive processes of translation. Singapur: Springer, 49–67. https://doi.org/10.1007/978-981-13-1984-6_3.10.1007/978-981-13-1984-6_3Search in Google Scholar

Carmo, Félix do/Moorkens, Joss (2021): Differentiating editing, post-editing and revision. In: Koponen, Maarit/Mossop, Brian/Robert, Isabelle/Scocchera, Giovanna (Eds.): Translation revision and post-editing. Industry practices and cognitive processes. London: Routledge, 35–49. https://doi.org/10.4324/9781003096962.10.4324/9781003096962-4Search in Google Scholar

Castaño, M. Asunción/Casacuberta, Francisco/Vidal, Enrique (1997): Machine translation using neural networks and finite-state models. In: Proceedings of the 7th conference on theoretical and methodological issues in machine translation of natural languages, 160–167. https://aclanthology.org/1997.tmi-1.19/ (25 May 2023).Search in Google Scholar

Chowdhery, Aakanksha/Narang, Sharan/Devlin, Jacob/Bosma, Maarten/Mishra, Gaurav/Roberts, Adam/Barham, Paul/Chung, Hyung Won/Sutton, Charles/Gehrmann, Sebastian/Schuh, Parker/Shi, Kensen/Tsvyashchenko, Sasha/Maynez, Joshua/Rao, Abhishek/Barnes, Parker/Tay, Yi/Shazeer, Noam/Prabhakaran, Vinodkumar/Reif, Emily/Du, Nan/Hutchinson, Ben/Pope, Reiner/Bradbury, James/Austin, Jacob/Isard, Michael/Gur-Ari, Guy/Yin, Pengcheng/Duke, Toju/Levskaya, Anselm/Ghemawat, Sanjay/Dev, Sunipa/Michalewski, Henryk/Garcia, Xavier/Misra, Vedant/Robinson, Kevin/Fedus, Liam/Zhou, Denny/Ippolito, Daphne/Luan, David/Lim, Hyeontaek/Zoph, Barret/Spiridonov, Alexander/Sepassi, Ryan/Dohan, David/Agrawal, Shivani/Omernick, Mark/Dai, Andrew M./Pillai, Thanumalayan Sankaranarayana/Pellat, Marie/Lewkowycz, Aitor/Moreira, Erica/Child, Rewon/Polozov, Oleksandr/Lee, Katherine/Zhou, Zongwei/Wang, Xuezhi/Saeta, Brennan/Diaz, Mark/Firat, Orhan/Catasta, Michele/Wie, Jason/Meier-Hellstern, Kathy/Eck, Douglas/Dean, Jeffrey/Petrov, Slav/Fiedel, Noah (2022): PaLM: Scaling language models with pathways. arXiv. https://doi.org/10.48550/arXiv.2204.02311.Search in Google Scholar

Chen, Pinzhen/Guo, Zhicheng/Haddow, Barry/Heafield, Kenneth (2023): Iterative translation refinement with large language models. arXiv. https://doi.org/10.48550/arXiv.2306.03856.Search in Google Scholar

Custom.MT (2023): ChatGPT in localization part II. Custom.MT. https://www.youtube.com/watch?v=bPeGctGTHz0 (22 June 2023).Search in Google Scholar

Devlin, Jacob/Chang, Ming-Wei/Lee, Kenton/Toutanova, Kristina (2019): BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, Jill/Doran, Christy/Solorio, Thamar(Eds.):Proceedings of the 2019 conference of the North American Chapter of the Association for Computational Linguistics: Human language technologies: Volume 1 (Long and short papers). Association for Computational Linguistics, 4171–4186. https://aclanthology.org/N19-1423/ (7 May 2023).Search in Google Scholar

ELIS Research (2023): European language industry survey 2023. https://elis-survey.org/ (22 May 2023).Search in Google Scholar

Eloundou, Tyna/Manning, Sam/Mishkin, Pamela/Rock, Daniel (2023): GPTs are GPTs: An early look at the labor market impact potential of large language models. arXiv. https://doi.org/10.48550/arXiv.2303.10130.Search in Google Scholar

European Parliamentary Research Service (2022): Artificial intelligence act. European Parliament. https://www.europarl.europa.eu/RegData/etudes/BRIE/2021/698792/EPRS_BRI(2021)698792_EN.pdf (17 June 2023).Search in Google Scholar

European Parliamentary Research Service (2023): General-purpose artificial intelligence. European Parliament. https://www.europarl.europa.eu/RegData/etudes/ATAG/2023/745708/EPRS_ATA(2023)745708_EN.pdf (17 June 2023).Search in Google Scholar

Forcada, Mikel L./Ñeco, Ramón P. (1997): Recursive hetero-associative memories for translation. In: Mira, José/Moreno-Díaz, Roberto/Cabestany, Joan (Eds.): Biological and artificial computation: From neuroscience to technology. Berlin: Springer, 453–462. https://doi.org/10.1007/BFb0032504.10.1007/BFb0032504Search in Google Scholar

Fu, Jinlan/Ng, See-Kiong/Jiang, Zhengbao/Liu, Pengfei (2023): GPTScore: Evaluate as you desire. arXiv. https://doi.org/10.48550/arXiv.2302.04166.Search in Google Scholar

Gao, Yuan/Wang, Ruili/Hou, Feng (2023): How to design translation prompts for ChatGPT: An empirical study. arXiv. https://doi.org/10.48550/arXiv.2304.02182.Search in Google Scholar

Ghazvininejad, Marjan/Gonen, Hila/Zettlemoyer, Luke (2023): Dictionary-based phrase-level prompting of large language models for machine translation. arXiv. https://doi.org/10.48550/arXiv.2302.07856.Search in Google Scholar

Goel, Ashok K. (2022): Looking back, looking ahead: Symbolic versus connectionist AI. AI Magazine 42(4), 83–85. https://doi.org/10.1609/aaai.12026.10.1609/aimag.v42i4.15111Search in Google Scholar

Goodfellow, Ian/Bengio, Yoshua/Courville, Aaron (2016): Deep learning. MIT Press. https://www.deeplearningbook.org/ (26 May 2023).Search in Google Scholar

Google (2023): PaLM 2 technical report. Google AI. https://ai.google/static/documents/palm2techreport.pdf (17 May 2023).Search in Google Scholar

Gottfredson, Linda S. (1997): Mainstream science on intelligence: An editorial with 52 signatories, history, and bibliography. Intelligence 24(1), 13–23. https://doi.org/10.1016/S0160-2896(97)90011–8.10.1016/S0160-2896(97)90011-8Search in Google Scholar

Guerreiro, Nuno M./Alves, Duarte M./Waldendorf, Jonas/Haddow, Barry/Birch, Alexandra/Colombo, Pierre/Martins, André F. T. (2023): Hallucinations in large multilingual translation models. arXiv. https://doi.org/10.48550/arXiv.2303.16104.10.1162/tacl_a_00615Search in Google Scholar

Hackenbuchner, Janiça/Krüger, Ralph (2023): DataLit^MT – Teaching data literacy in the context of machine translation literacy. In: Nurminen, Mary/Brenner, Judith/Koponen, Maarit/Latomaa, Sirkku/Mikhailov, Mikhail/Schierl, Frederike/Ranasinghe, Tharindu/Vanmassenhove, Eva/Alvarez Vidal, Sergi/Aranberri, Nora/Nunziatini, Mara/Parra Escartìn, Carla/Forcada, Mikel/Popovic, Maja/Scarton, Carolina/Moniz, Helena (Eds.): Proceedings of the 24th annual conference of the European Association for Machine Translation (EAMT 2023). European Association for Machine Translation, 285–293. https://events.tuni.fi/uploads/2023/06/a52469c0-proceedings-eamt2023.pdf (9 June 2023).Search in Google Scholar

He, Zhiwei/Liang, Tian/Jiao, Wenxiang/Zhang, Zhuosheng/Yang, Yujiu/Wang, Rui/Tu, Zhaopeng/Shi, Shuming/Wang, Xing (2023): Exploring human-like translation strategy with large language models. arXiv. https://doi.org/10.48550/arXiv.2305.04118.Search in Google Scholar

Hendy, Amr/Abdelrehim, Mohamed/Sharaf, Amr/Raunak, Vikas/Gabr, Mohamed/Matsushita, Hitokazu/Jin Kim, Young/Afify, Mohamed/Hassan Awadalla, Hany (2023): How good are GPT models at machine translation? A comprehensive evaluation. arXiv. https://doi.org/10.48550/arXiv.2302.09210.Search in Google Scholar

Hoffmann, Jordan/Borgeaud, Sebastian/Mensch, Arthur/Buchatskaya, Elena/Cai, Trevor/Rutherford, Eliza/de Las Casas, Diego/Hendricks, Lisa Anne/Welbl, Johannes/Clark, Aidan/Hennigan, Tom/Noland, Eric/Millican, Katie/van den Driessche, George/Damoc, Bogdan/Guy, Aurelia/Osindero, Simon/Simonyan, Karen/Elsen, Erich/Rae, Jack W./Vinyals, Oriol/Sifre, Laurent (2022): Training compute-optimal large language models. arXiv. https://doi.org/10.48550/arXiv.2203.15556.Search in Google Scholar

Hornik, Kurt/Stinchcombe, Maxwell/White, Halbert (1989): Multilayer feedforward networks are universal approximators. Neural Networks 2(5), 359–366. https://doi.org/10. 1016/0893-6080(89)90020–8.10.1016/0893-6080(89)90020-8Search in Google Scholar

Jedamski, Derek (2020): Advanced NLP with Python for machine learning. LinkedIn Learning online course. https://www.linkedin.com/learning/advanced-nlp-with-python-for-machine-learning/ (26 May 2023).Search in Google Scholar

Jiao, Wenxiang/Huang, Jen-tse/Wang, Wenxuan/Wang, Xing/Shi, Shuming/Tu, Zhaopeng (2023): ParroT: Translating during chat using large language models. arXiv. https://doi.org/10.48550/arXiv.2304.02426.10.18653/v1/2023.findings-emnlp.1001Search in Google Scholar

Jiao, Wenxiang/Wang, Wenxuan/Huang, Jen-tse/Wang, Xing/Tu, Zhaopeng (2023): Is ChatGPT a good translator? Yes with GPT-4 as the engine. arXiv. https://doi.org/10.48550/arXiv.2301.08745.Search in Google Scholar

Kahneman, Daniel (2011): Thinking, fast and slow. New York: McMillan.Search in Google Scholar

Kaplan, Jared/McCandlish, Sam/Henighan, Tom/Brown, Tom B./Chess, Benjamin/Child, Rewon/Gray, Scott/Radford, Alec/Wu, Jeffrey/Amodei, Dario (2020): Scaling laws for neural language models. arXiv. https://doi.org/10.48550/arXiv.2001.08361.Search in Google Scholar

Karpathy, Andrej (2023): The hottest new programming language is English. Twitter. https://twitter.com/karpathy/status/1617979122625712128 (7 May 2023).Search in Google Scholar

Kinsley, Harrison/Kukiela, Daniel (2020): Neural networks from scratch in Python. https://nnfs.io/ (7 May 2023).Search in Google Scholar

Kocmi, Tom/Federmann, Christian (2023): Large language models are state-of-the-art evaluators of translation quality. In: Nurminen, Mary/Brenner, Judith/Koponen, Maarit/Latomaa, Sirkku/Mikhailov, Mikhail/Schierl, Frederike/Ranasinghe, Tharindu/Vanmassenhove, Eva/Alvarez Vidal, Sergi/Aranberri, Nora/Nunziatini, Mara/Parra Escartìn, Carla/Forcada, Mikel/Popovic, Maja/Scarton, Carolina/Moniz, Helena (Eds.): Proceedings of the 24th annual conference of the European Association for Machine Translation (EAMT 2023). European Association for Machine Translation, 193–203. https://events.tuni.fi/uploads/2023/06/a52469c0-proceedings-eamt2023.pdf (19 June 2023).Search in Google Scholar

Koehn, Philipp (2020): Neural machine translation. Cambridge: University Press.10.1017/9781108608480Search in Google Scholar

Kojima, Takeshi/Gu, Shixiang Shane/Reid, Machel/Matsuo, Yutaka/Iwasaka, Yusuke (2022): Large language models are zero-shot reasoners. arXiv. https://doi.org/10.48550/arXiv.2205.11916.Search in Google Scholar

Konishi, Yoko (2015): What is needed for AI literacy? Priorities for the Japanese economy in 2016. Research Institute of Economy, Trade and Industry. https://www.rieti.go.jp/en/columns/s16_0014.html (14 June 2023).Search in Google Scholar

Krizhevsky, Alex/Sutskever, Ilya/Hinton, Geoffrey E. (2012): ImageNet classification with deep convolutional neural networks. In: Pereira, Fernando/Burges, Christopher J./Bottou, Leon/Weinberger, Kilian Q. (Eds.): Advances in neural information processing systems 25 (NIPS 2012), 1–9. https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html (25 May 2023).Search in Google Scholar

Krüger, Ralph (2018): Technologieinduzierte Verschiebungen in der Tektonik der Translationskompetenz. trans-kom 11(1), 104–137. http://www.trans-kom.eu/bd11nr01/trans-kom_11_01_06_Krueger_Tektonik.20180712.pdf (9 June 2023).Search in Google Scholar

Krüger, Ralph (2022a): Some translation studies informed suggestions for further balancing methodologies for machine translation quality evaluation. Translation Spaces 11(2), 213–233. https://doi.org/10.1075/ts.21026.kru.10.1075/ts.21026.kruSearch in Google Scholar

Krüger, Ralph (2022b): Integrating professional machine translation literacy and data literacy. Lebende Sprachen 67(2), 247–282. https://doi.org/10.1515/les-2022–1022.10.1515/les-2022-1022Search in Google Scholar

Krüger, Ralph/Hackenbuchner, Janiça (2022): Outline of a didactic framework for combined data literacy and machine translation literacy teaching. Current Trends in Translation Teaching and Learning E 2022, 375–432. https://doi.org/10.51287/cttl202211.10.51287/cttl202211Search in Google Scholar

Laupichler, Matthias Carl/Aster, Alexandra/Schirch, Jana/Raupach, Tobias (2022): Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence 3, 1–15. https://doi.org/10.1016/j.caeai.2022.100101.10.1016/j.caeai.2022.100101Search in Google Scholar

LeCun, Yann/Bengio, Yoshua/Hinton, Geoffrey (2015): Deep learning. Nature 521, 436–444. https://doi.org/10.1038/nature14539.10.1038/nature14539Search in Google Scholar

Lewis, Mike/Liu, Yinhan/Goyal, Naman/Ghazvininejad, Marjan/Mohamed, Abdelrahman/Levy, Omer/Stoyanov, Veselin/Zettlemoyer, Luke (2020): BART: Denoising sequence-to-sequence pre-training for natural language generation, translation and comprehension. In: Jurafsky, Dan/Chai, Joyce/Schluter, Natalie/Tetreault, Joel (Eds.): Proceedings of the 58th annual meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 7871–7880. http://dx.doi.org/10.18653/v1/2020.acl-main.703.10.18653/v1/2020.acl-main.703Search in Google Scholar

Liu, Pengfei/Yuan, Weizhe/Fu, Jinlan/Jiang, Zhengbao/Hayashi, Hiroaki/Neubig, Graham (2021): Pre-train, prompt, and predict: A systematic survey of prompt methods in natural language processing. ACM Computing Surveys 55(9), 1–35. https://doi.org/10.1145/3560815.10.1145/3560815Search in Google Scholar

Liu, Nelson F./Lin, Kevin/Hewitt, John/Paranjape, Ashwin/Bevilacqua, Michele/Petroni, Fabio/Liang, Percy (2023): Lost in the middle: How language models use long contexts. arXiv. https://doi.org/10.48550/arXiv.2307.03172.Search in Google Scholar

Long, Duri/Magerko, Brian (2020): What is AI literacy? Competencies and design considerations. In: Bernhaupt, Regina/Mueller, Florian/Verweij, David/Andres, Josh (Eds.): CHI '20: Proceedings of the 2020 CHI conference on human factors in computing systems. Association for Computing Machinery, 1–16. https://doi.org/10.1145/3313831.3376727.10.1145/3313831.3376727Search in Google Scholar

Lu, Qinghua/Zhu, Liming/Xu, Xiwei/Xing, Zhenchang/Whittle, Jon (2023): Towards responsible AI in the era of ChatGPT: A reference architecture for designing foundation model-based AI systems. arXiv. https://doi.org/10.48550/arXiv.2304.11090.Search in Google Scholar

Lu, Qingyu/Qiu, Baopu/Ding, Liang/Xie, Liping/Tao, Dacheng (2023): Error analysis prompting enables human-like translation evaluation in large language models: A case study on ChatGPT. arXiv. https://doi.org/10.48550/arXiv.2303.13809.10.20944/preprints202303.0255.v1Search in Google Scholar

Luong, Minh-Thang/Manning, Christopher (2015): Stanford neural machine translation systems for spoken language domains. In: Federico, Marcello/Stüker, Sebastian/Niehues, Jan (Eds.): Proceedings of the 12^th international workshop on spoken language translation: Evaluation campaign, 76–79. https://aclanthology.org/2015.iwslt-evaluation.11 (25 May 2023).Search in Google Scholar

Markauskaite, Lina/Marrone, Rebecca/Poquet, Oleksandra/Knight, Simon/Martinez-Maldonado, Roberto/Howard, Sarah/Tondeur, Jo/De Laat, Maarten/Buckingham Shum, Simon/Gasevic, Dragan/Siemens, George (2022): Rethinking the entwinement between artificial intelligence and human learning: What capabilities do learners need for a world with AI? Computers and Education: Artificial Intelligence 3, 1–16. https://doi.org/10.1016/j.caeai.2022.100056.10.1016/j.caeai.2022.100056Search in Google Scholar

Maslej, Nestor/Fattorini, Loredana/Brynjolfsson, Erik/Etchemendy, John/Ligett, Katrina/Lyons, Terah/Manyika, James/Ngo, Helen/Niebles, Juan Carlos/Parli, Vanessa/Shoham, Yoav/Wald, Russell/Clark, Jack/Perrault, Raymond (2023): The AI index 2023 annual report. AI Index Steering Committee, Institute for Human-Centered AI, Stanford University. https://aiindex.stanford.edu/report/ (6 May 2023).Search in Google Scholar

McCarthy, John/Minsky, Marvin L./Rochester, Nathaniel/Shannon, Claude E. (1955): A proposal for the Dartmouth summer research project on artificial intelligence. http://jmc.stanford.edu/articles/dartmouth/dartmouth.pdf (22 May 2023).Search in Google Scholar

McCulloch, Warren S./Pitts, Walter (1943): A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics 5, 115–133. https://doi.org/10.1007/BF02478259.10.1007/BF02478259Search in Google Scholar

Mikolov, Tomas/Sutskever, Ilya/Chen, Kai/Corrado, Greg/Dean, Jeffrey (2013): Distributed representations of words and phrases and their compositionality. In: Burges, Christopher J./Bottou, Leon/Welling, Max/Ghahramani, Zoubin/Weinberger, Kilian Q. (Eds.): Advances in neural information processing systems 26 (NIPS 2013), 1–9. https://papers.nips.cc/paper_files/paper/2013/hash/9aa42b31882ec039965f3c4923ce901b-Abstract.html (5 June 2023).Search in Google Scholar

Minsky, Marvin L. (1968) (Ed.): Semantic information processing. Cambridge: MIT Press.Search in Google Scholar

Minsky, Marvin L. (1991): Logical versus analogical or symbolic versus connectionist or neat versus scruffy. AI Magazine 12(2), 34–51. https://ojs.aaai.org/aimagazine/index.php/aimagazine/issue/view/92 (22 May 2023).Search in Google Scholar

Minsky, Marvin L./Papert, Seymour A. (1969): Perceptrons: An introduction to computational geometry. Cambridge: MIT Press. https://doi.org/10.7551/mitpress/11301.001.0001.10.7551/mitpress/11301.001.0001Search in Google Scholar

Moorkens, Joss (2022): Ethics and machine translation. In: Kenny, Dorothy (Ed.): Machine translation for everyone. Empowering users in the age of artificial intelligence. Berlin: Language Science Press, 121–140. https://doi.org/10.5281/zenodo.6653406.Search in Google Scholar

Moslem, Yasmin/Haque, Rejwanul/Kelleher, John D./Way, Andy (2023): Adaptive machine translation with large language models. In: Nurminen, Mary/Brenner, Judith/Koponen, Maarit/Latomaa, Sirkku/Mikhailov, Mikhail/Schierl, Frederike/Ranasinghe, Tharindu/Vanmassenhove, Eva/Alvarez Vidal, Sergi/Aranberri, Nora/Nunziatini, Mara/Parra Escartìn, Carla/Forcada, Mikel/Popovic, Maja/Scarton, Carolina/Moniz, Helena (Eds.): Proceedings of the 24th annual conference of the European Association for Machine Translation (EAMT 2023). European Association for Machine Translation, 227–237. https://events.tuni.fi/uploads/2023/06/a52469c0-proceedings-eamt2023.pdf (19 June 2023).Search in Google Scholar

Neubig, Graham/He, Zhiwei (2023): Zeno GPT-MT report. GitHub. https://github.com/zeno-ml/zeno-build/tree/main/examples/analysis_gpt_mt/report (15 July 2023).Search in Google Scholar

Ng, Davy Tsz Kit/Leung, Jac Ka Lok/Chu, Samuel Kai Wah/Qiao, Maggie Shen (2021): Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence 2, 1–11. https://doi.org/10.1016/j.caeai.2021.100041.10.1016/j.caeai.2021.100041Search in Google Scholar

Omniscien Technologies (2023): The future of language related AI for enterprises: Local agents and fine-tuned large language models (LLMs). https://docs.omniscien.com/videos/ (19 June 2023).Search in Google Scholar

OpenAI (n. d.): Models overview. OpenAI. https://platform.openai.com/docs/models/overview (19 June 2023).Search in Google Scholar

OpenAI (2020): Language distribution of GPT-3 training data. https://github.com/openai/gpt-3/blob/master/dataset_statistics/languages_by_character_count.csv (12 June 2023).Search in Google Scholar

OpenAI (2022): Introducing ChatGPT. OpenAI Blog. https://openai.com/blog/chatgpt (7 May 2023).Search in Google Scholar

OpenAI (2023): GPT-4 technical report. OpenAI. https://cdn.openai.com/papers/gpt-4.pdf (7 May 2023).Search in Google Scholar

Ouyang, Long/Wu, Jeff/Jiang, Xu/Almeida, Diogo/Wainwright, Carroll L./Mishkin, Pamela/Zhang, Chong/Agarwal, Sandhini/Slama, Katarina/Ray, Alex/Schulman, John/Hilton, Jacob/Kelton, Fraser/Miller, Luke/Simens, Maddie/Askell, Amanda/Welinder, Peter/Christiano, Paul/Leike, Jan/Lowe, Ryan (2022): Training language models to follow instructions with human feedback. OpenAI. https://cdn.openai.com/papers/Training_language_models_to_follow_instructions_with_human_feedback.pdf (7 May 2023).Search in Google Scholar

Peng, Keqin/Ding, Liang/Zhong, Qihuang/Shen, Li/Liu, Xuebo/Zhang, Min/Ouyang, Yuanxin/Tao, Dacheng (2023): Towards making the most of ChatGPT for machine translation. arXiv. https://doi.org/10.48550/arXiv.2303.13780.10.2139/ssrn.4390455Search in Google Scholar

Pérez-Ortiz, Juan Antonio/Forcada, Mikel L./Sánchez-Martinez, Felipe (2022): How neural machine translation works. In: Kenny, Dorothy (Ed.): Machine translation for everyone. Empowering users in the age of artificial intelligence. Berlin: Language Science Press, 141–164. https://doi.org/10.5281/zenodo.6653406.Search in Google Scholar

Pryzant, Reid/Iter, Dan/Li, Jerry/Lee, Yin Tat/Zhu, Chenguang/Zeng, Michael (2023): Automatic prompt optimization with “gradient descent” and beam search. arXiv. https://doi.org/10.48550/arXiv.2305.03495.10.18653/v1/2023.emnlp-main.494Search in Google Scholar

Radford, Alec/Narasimhan, Karthik/Salimans, Tim/Sutskever, Ilya (2018): Improving language understanding by generative pre-training. OpenAI. https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf (7 May 2023).Search in Google Scholar

Radford, Alec/Wu, Jeffrey/Child, Rewon/Luan, David/Amodei, Dario/Sutskever, Ilya (2019): Language models are unsupervised multitask learners. OpenAI. https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf (6 May 2023).Search in Google Scholar

Raunak, Vikas/Sharaf, Amr/Awadallah, Hany Hassan/Menezes, Arul (2023): Leveraging GPT-4 for automatic translation post-editing. arXiv. https://doi.org/10.48550/arXiv.2305.14878.10.18653/v1/2023.findings-emnlp.804Search in Google Scholar

Reineke, Detlef (2023): Terminologiearbeit mit ChatGPT & Co. edition 19(1), 25–28. http://dttev.org/images/edition/ausgaben/edition-2023-1-e-version.pdf (2 June 2023).Search in Google Scholar

Ridsdale, Chantel/Rothwell, James/Smit, Mike/Ali-Hassan, Hossam/Bliemel, Michael/Irvine, Dean/Kelley, Daniel/Matwin, Stan/Wuetherick, Brad (2015): Strategies and best practices for data literacy education. Knowledge synthesis report. Dalhousie University. http://hdl.handle.net/10222/64578 (15 June 2023).Search in Google Scholar

Rosenblatt, Frank (1958): The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65(6), 386–408. https://psycnet.apa.org/doi/10.1037/h0042519.10.1037/h0042519Search in Google Scholar

Rumelhart, David E./Hinton, Geoffrey E./Williams, Ronald J. (1986): Learning representations by back-propagating errors. Nature 323, 533–536. https://doi.org/10.1038/323533a0.10.1038/323533a0Search in Google Scholar

Rumelhart, David E./McClelland, James L./PDP Research Group (1987): Parallel distributed processing. Explorations in the microstructure of cognition. Volume 1: Foundations. Cambridge: MIT Press. https://mitpress.mit.edu/9780262680530/parallel-distributed-processing/ (25 May 2023).Search in Google Scholar

Russell, Stuart/Norvig, Peter (1995/2021): Artificial intelligence: A modern approach, global edition. 4^th edition. Harlow: Pearson.Search in Google Scholar

Schaeffer, Rylan/Miranda, Brando/Koyejo, Sanmi (2023): Are emergent abilities of large language models a mirage? arXiv. https://doi.org/10.48550/arXiv.2304.15004.Search in Google Scholar

Significant Gravitas (2023): Auto-GPT: An autonomous GPT-4 experiment. GitHub repository. https://github.com/Significant-Gravitas/Auto-GPT (12 June 2023).Search in Google Scholar

Skansi, Sandro (2018): Introduction to deep learning. From logical calculus to artificial intelligence. Cham: Springer. https://doi.org/10.1007/978-3-319-73004-2.10.1007/978-3-319-73004-2Search in Google Scholar

Slator (2022): Slator machine translation expert-in-the-loop report. Slator. https://slator.com/machine-translation-expert-in-the-loop-report/ (22 June 2023).Search in Google Scholar

SmartCat (2023): What AI, automation, and ChatGPT really mean for localization. SmartCat. https://www.smartcat.com/ai-chatgpt-automation-translation-whitepaper/ (23 June 2023).Search in Google Scholar

Stasimioti, Maria (2023): Here are 30 new jobs language industry CEOs expect to hire for in the AI age. Slator. https://slator.com/30-new-jobs-language-industry-ceos-expect-to-hire-ai-age/ (12 June 2023).Search in Google Scholar

Stonier, Tom (1992): Beyond information: The natural history of intelligence. London: Springer, 107–133. https://doi.org/10.1007/978-1-4471-1835-0.10.1007/978-1-4471-1835-0_6Search in Google Scholar

Toral, Antonio (2019): Post-editese: An exacerbated translationese. In: Forcada, Mikel/Way, Andy/Haddow, Barry/Sennrich, Rico (Eds.): Proceedings of machine translation summit XVII: Research track. European Association for Machine Translation, 273–281. https://aclanthology.org/W19-6627/ (9 June 2023).Search in Google Scholar

Turing, Alan M. (1950): Computing machinery and intelligence. Mind 59(236), 433–460. https://doi.org/10.1093/mind/LIX.236.433.10.1093/mind/LIX.236.433Search in Google Scholar

Unbabel (n. d.): Quality intelligence report. Score your translation quality, instantly. Unbabel. https://qi.unbabel.com/ (22 June 2023).Search in Google Scholar

Uszkoreit, Jakob (2017): Transformer: A novel neural network architecture for language understanding. Google AI Blog. https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html (22 May 2023).Search in Google Scholar

van der Meer, Jaap (2019): Translation technology – Past, present and future. In: Angelone, Erik/Ehrensberger-Dow, Maureen/Massey, Gary (Eds.): The Bloomsbury companion to language industry studies. London: Bloomsbury Academic, 285–310. http://dx.doi.org/10.5040/9781350024960.10.5040/9781350024960.0017Search in Google Scholar

Vaswani, Ashish/Shazeer, Noam/Parmar, Niki/Uszkoreit, Jakob/Jones, Llion/Gomez, Aidan N./Kaiser, Łukasz/Polosukhin, Łukasz (2017): Attention is all you need. In: Guyon, Isabelle/Luxburg, Ulrike von/Bengio, Samy/Wallach, Hanna M./Fergus, Rob/Vishwanathan, S. V. N./Garnett, Roman (Eds.): Advances in neural information processing systems 30 (NIPS 2017), 1–11. https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html (06 May 2022).Search in Google Scholar

Vilar, David/Freitag, Markus/Cherry, Colin/Luo, Jiaming/Ratnakar, Viresh/Foster, George (2022): Prompting PaLM for translation: Assessing strategies and performance. arXiv. https://doi.org/10.48550/arXiv.2211.09102.10.18653/v1/2023.acl-long.859Search in Google Scholar

Walker Rettberg, Jill (2022): ChatGPT is multilingual but monocultural, and it’s learning your values. jill/txt blog. https://jilltxt.net/right-now-chatgpt-is-multilingual-but-monocultural-but-its-learning-your-values/ (12 June 2023).Search in Google Scholar

Wang, Longyue/Lyu, Chenyang/Ji, Tianbo/Zhang, Zhirui/Yu, Dian/Shi, Shuming/Tu, Zhaopeng (2023): Document-level machine translation with large language models. arXiv. https://doi.org/10.48550/arXiv.2304.02210.10.18653/v1/2023.emnlp-main.1036Search in Google Scholar

Way, Andy/Forcada, Mikel L. (2018): Editors’ foreword to the invited issue on SMT and NMT. Machine Translation 32(3), 191–194. https://doi.org/10.1007/s10590-018-9222-x.10.1007/s10590-018-9222-xSearch in Google Scholar

Wei, Jason/Tay, Yi/Bommasani, Rishi/Raffel, Colin/Zoph, Barret/Borgeaud, Sebastian/Yogatama, Dani/Bosma, Maarten/Zhou, Denny/Metzler, Donald/Chi, Ed H./Hashimoto, Tatsunori/Vinyals, Oriol/Liang, Percy/Dean, Jeff/Fedus, William (2022): Emergent abilities of large language models. arXiv. https://arxiv.org/abs/2206.07682 (2 June 2023).Search in Google Scholar

Wei, Jason/Wang, Xuezhi/Schuurmans, Dale/Bosma, Maarten/Ichter, Brian/Xia, Fei/Chi, Ed/Le, Quoc/Zhou, Denny (2023): Chain-of-thought prompting elicits reasoning in large language models. arXiv. https://doi.org/10.48550/arXiv.2201.11903.Search in Google Scholar

Wolfram, Stephen (2023): How ChatGPT changes the future of programming. Interview of Lex Fridman with Stephen Wolfram. Lex Clips. https://www.youtube.com/watch?v=e-QPQpEI4WM&t=09m42s (12 June 2023).Search in Google Scholar

Wyndham, Anna (2023): How many language service providers have piloted ChatGPT in workflows? Slator. https://slator.com/how-many-language-service-providers-have-piloted-chatgpt-workflows/ (5 June 2023).Search in Google Scholar

Yan, Da/Wang, Junyue (2022): Teaching data science to undergraduate translation trainees: Pilot evaluation of a task-based course. Frontiers in Psychology 13, 1–18. https://doi.org/10.3389/fpsyg.2022.939689.10.3389/fpsyg.2022.939689Search in Google Scholar

Zhang, Biao/Haddow, Barry/Birch, Alexandra (2023): Prompting large language model for machine translation: A case study. arXiv. https://doi.org/10.48550/arXiv.2301.07069.Search in Google Scholar

Zhao, Wayne Xin/Zhou, Kun/Li, Junyi/Tang, Tianyi/Wang, Xiaolei/Hou, Yupeng/Min, Yingqian/Zhang, Beichen/Zhang, Junjie/Dong, Zican/Du, Yifan/Yang, Chen/Chen, Yushuo/Chen, Zhipeng/Jiang, Jinhao/Ren, Ruiyang/Li, Yifan/Tang, Xinyu/Liu, Zikang/Liu, Peiyu/Nie, Jian-Yun/Wen, Ji-Rong (2023): A survey of large language models. arXiv. https://doi.org/10.48550/arXiv.2303.18223.Search in Google Scholar

Zhou, Yongchao/Muresanu, Andrei Ioan/Han, Ziwen/Paster, Keiran/Pitis, Silviu/Chan, Harris/Ba, Jimmy (2023): Large language models are human-level prompt engineers. arXiv. https://doi.org/10.48550/arXiv.2211.01910 (12 June 2023).Search in Google Scholar