Elamur Research⌕
☰
home / papers / 2023 / Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages

Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages

Paper · 2023 · Zheng-Xin Yong, Ruochen Zhang, Jessica Zosa Forde et al.

Summary

Code-mixing, or switching between languages within a single utterance, is common across South East Asia. Yet NLP research on it has little data, because such speech is mostly informal or spoken, and scraping it from social media raises legal and scaling problems. The authors ask whether instruction-tuned multilingual LLMs can fill this gap. They used English-language prompt templates in a zero-shot setting, with no examples. Each prompt varied the framing (for instance, posing as a bilingual speaker or imitating a speaking style) and the topic (such as food, traffic, family, weather and AI). The prompts targeted English mixed with six SEA languages, plus Singlish. Native speakers then rated each output for naturalness and degree of mixing. Openly available models mostly failed to produce real bilingual text. ChatGPT performed best but unreliably: results depended on the template and the language pair, and grammar and meaning errors were frequent. The study concludes that synthetic code-mixed data from LLMs is not trustworthy without native-speaker review and editing.

Key points

  • Languages: Indonesian, Malay, Chinese, Tagalog, Vietnamese and Tamil, each paired with English, plus Singlish (an English-based creole of Singapore). Together they cover Brunei, Indonesia, Malaysia, the Philippines, Singapore and Vietnam.
  • Models compared: ChatGPT, InstructGPT (davinci-002 and davinci-003), BLOOMZ and Flan-T5-XXL.
  • Open models: BLOOMZ and Flan-T5-XXL rarely went beyond inserting loanwords or topic nouns. The authors suspect their pretraining and instruction-tuning data lack code-mixed text.
  • ChatGPT by language pair: It wrote fluent, natural Singlish. For English-Tamil, however, it mostly produced ungrammatical or meaningless output, and it sometimes added languages the prompt never requested.
  • Stated limitations: The study did not test the generated data on downstream tasks or compare it with human-written code-mixed text. It also covered only English-centric pairs, English zero-shot prompts and instruction-tuned models.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Stanford UniversityStanford · United States434 works · 314,419 citations🎓University of California, BerkeleyBerkeley · United States175 works · 227,440 citations🎓National University of SingaporeSingapore · Singapore180 works · 34,304 citations🎓Brown UniversityProvidence · United States76 works · 25,952 citations🎓Hong Kong University of Science and TechnologyHong Kong · China142 works · 25,136 citations🎓University of California, Los AngelesLos Angeles · United States87 works · 54,961 citations🎓University of PotsdamPotsdam · Germany44 works · 5,305 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksMultilingual & cross-lingual NLP · 828 works

Cite

Cite as Zheng-Xin Yong, Ruochen Zhang, Jessica Zosa Forde, Skyler Wang, Arjun Subramonian, Holy Lovenia, Samuel Cahyawijaya, Genta Indra Winata, Lintang Sutawika, Jan Christian Blaise Cruz, Yin Lin Tan, Long Phan, Rowena Garcia, Thamar Solorio and Alham Fikri Aji. 2023. Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages. arXiv:2303.13592.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2303.13592 · arXiv 2303.13592

@misc{yong2023prompting,
  title = {{Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages}},
  author = {Zheng-Xin Yong, Ruochen Zhang, Jessica Zosa Forde, Skyler Wang, Arjun Subramonian, Holy Lovenia, Samuel Cahyawijaya, Genta Indra Winata, Lintang Sutawika, Jan Christian Blaise Cruz, Yin Lin Tan, Long Phan, Rowena Garcia, Thamar Solorio and Alham Fikri Aji},
  year = {2023},
  doi = {10.48550/arxiv.2303.13592},
  eprint = {2303.13592},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2303.13592}
}

References

87 references, 17 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Muhammad Farid Adilazuarda, Samuel Cahyawijaya, Genta Indra Winata, Pascale Fung, and Ayu Purwarianti. 2022. IndoRobusta: Towards robustness against diverse code-mixed Indonesian local languages. In Proceedings of the First Workshop on Scaling Up Multilingual Evaluation, pages 25–34, Online. Associa… find
  2. Gustavo Aguilar, Sudipta Kar, and Thamar Solorio. 2020. LinCE: A centralized benchmark for linguistic code-switching evaluation. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1803–1813, Marseille, France. European Language Resources Association.
  3. Alham Fikri Aji, Genta Indra Winata, Fajri Koto, Samuel Cahyawijaya, Ade Romadhony, Rahmad Mahendra, Kemal Kurniawan, David Moeljadi, Radityo Eko Prasojo, Timothy Baldwin, Jey Han Lau, and Sebastian Ruder. 2022. One country, 700+ languages: NLP challenges for underrepresented languages and dialects… find
  4. Edith Aldridge. 2012. Antipassive and ergativity in tagalog. Lingua, 122:192–203. find
  5. Douglas G Altman. 1990. Practical statistics for medical research. CRC press. find
  6. Ramakrishna Appicharla, Kamal Kumar Gupta, Asif Ekbal, and Pushpak Bhattacharyya. 2021. IITP-MT at CALCS2021: English to Hinglish neural machine translation using unsupervised synthetic code-mixed parallel corpus. In Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Sw… find
  7. Eric Armstrong. Tamil and tamil-english accent. find
  8. Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, et al. 2023. A multi-task, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity. arXiv preprint arXiv:2302.04023.
  9. Anab Maulana Barik, Rahmad Mahendra, and Mirna Adriani. 2019. Normalization of Indonesian-English code-mixed Twitter data. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 417–424, Hong Kong, China. Association for Computational Linguistics. find
  10. John Baugh. 2005. Linguistic profiling. In Black linguistics, pages 167–180. Routledge. find
  11. Maria Lourdes S Bautista and Andrew B Gonzalez. 2006. Southeast asian englishes. The handbook of world Englishes, pages 130–144. find
  12. Anshul Bawa, Pranav Khadpe, Pratik Joshi, Kalika Bali, and Monojit Choudhury. 2020. Do multilingual users prefer chat-bots that code-mix? let’s nudge and find out! Proceedings of the ACM on Human-Computer Interaction, 4(CSCW1):1–23. find
  13. Susan Berk-Seligson. 1986. Linguistic constraints on intrasentential code-switching: A study of spanish/hebrew bilingualism. Language in society, 15(3):313–348. find
  14. T. K. Bhatia and W. C. Ritchie. 2004. Social and psychological factors in language mixing. In W. C. Ritchie and T. K. Bhatia, editors, Handbook of bilingualism, pages 336–352. Blackwell Publishing. find
  15. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901.
  16. Samuel Cahyawijaya, Holy Lovenia, Alham Fikri Aji, Genta Indra Winata, Bryan Wilie, Rahmad Mahendra, Christian Wibisono, Ade Romadhony, Karissa Vincentio, Fajri Koto, Jennifer Santoso, David Moeljadi, Cahya Wirawan, Frederikus Hudi, Ivan Halim Parmonangan, Ika Alfina, Muhammad Satrio Wicaksono, Ilha… find
  17. Joyce YC Chan, Houwei Cao, PC Ching, and Tan Lee. 2009. Automatic recognition of cantonese-english code-mixing speech. In International Journal of Computational Linguistics & Chinese Language Processing, Volume 14, Number 3, September 2009. find
  18. Su-Chiao Chen. 1996. Code-switching as a verbal strategy among chinese in a campus setting in taiwan. World Englishes, 15(3):267–280. find
  19. T Chen and Kan Min-Yen. 2015. The national university of singapore sms corpus. find
  20. Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022. Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416. find
  21. Xingwei He, Zhenghao Lin, Yeyun Gong, A Jin, Hang Zhang, Chen Lin, Jian Jiao, Siu Ming Yiu, Nan Duan, Weizhu Chen, et al. 2023. Annollm: Making large language models to be better crowdsourced annotators. arXiv preprint arXiv:2303.16854.
  22. Commonwealth of the Philippines. 1936. Commonwealth act no. 184: Govph. find
  23. Republik Indonesia. 2002. find
  24. Undang-Undang Dasar Negara Republik Indonesia Tahun 1945. Sekretariat Jenderal MPR RI. find
  25. Mona Diab, Julia Hirschberg, Pascale Fung, and Thamar Solorio, editors. 2014. Proceedings of the First Workshop on Computational Approaches to Code Switching. Association for Computational Linguistics, Doha, Qatar. find
  26. Mark Dingemanse and Andreas Liesenfeld. 2022. From text to talk: Harnessing conversational corpora for humane and diversity-aware language technology. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5614–5633, Dublin, Ireland.… find
  27. A. Seza Doğruöz, Sunayana Sitaram, Barbara E. Bullock, and Almeida Jacqueline Toribio. 2021. A survey of code-switching: Linguistic and social perspectives for language technologies. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th Internation…
  28. Ganesh Jawahar, El Moatez Billah Nagoudi, Muhammad Abdul-Mageed, and Laks Lakshmanan, V.S. 2021. Exploring text-to-text transformers for English to Hinglish machine translation with synthetic code-mixing. In Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching,… find
  29. Pratik Joshi, Sebastin Santy, Amar Budhiraja, Kalika Bali, and Monojit Choudhury. 2020. The state and fate of linguistic diversity and inclusion in the NLP world. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6282–6293, Online. Association for Comp…
  30. Anubha Kabra, Emmy Liu, Simran Khanuja, Alham Fikri Aji, Genta Indra Winata, Samuel Cahyawijaya, Anuoluwapo Aremu, Perez Ogayo, and Graham Neubig. 2023. Multi-lingual and multi-cultural figurative language understanding. find
  31. Suman Dowlagar and Radhika Mamidi. 2021. Gated convolutional sequence to sequence based learning for English-hingilsh code-switched machine translation. In Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching, pages 26–30, Online. Association for Computational L… find
  32. Simran Khanuja, Sandipan Dandapat, Anirudh Srinivasan, Sunayana Sitaram, and Monojit Choudhury. 2020. GLUECoS: An evaluation benchmark for code-switched NLP. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3575–3585, Online. Association for Computati…
  33. Joseph L Fleiss. 1971. Measuring nominal scale agreement among many raters. Psychological bulletin, 76(5):378. find
  34. Andy Kirkpatrick. 2014. English in southeast asia: Pedagogical and policy implications. World Englishes, 33(4):426–438. find
  35. Björn Gambäck and Amitava Das. 2014. On measuring the complexity of code-mixing. In Proceedings of the 11th international conference on natural language processing, Goa, India, pages 1–7. find
  36. Paul Kroeger. 1993. Phrase structure and grammatical relations in Tagalog. Center for the Study of Language (CSLI). find
  37. Devansh Gautam, Prashant Kodali, Kshitij Gupta, Anmol Goel, Manish Shrivastava, and Ponnurangam Kumaraguru. 2021. CoMeT: Towards code-mixed translation using parallel monolingual sentences. In Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching, pages 47– 55, O… find
  38. Cliff Goddard. 2005. The languages of East and Southeast Asia: an introduction. Oxford University Press on Demand. find
  39. F. Grosjean. 1982. Life with two languages: An introduction to bilingualism. Harvard University Press. find
  40. Huiyuan Lai and Malvina Nissim. 2022. Multi-figurative language generation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5939–5954, Gyeongju, Republic of Korea. International Committee on Computational Linguistics. find
  41. J Richard Landis and Gary G Koch. 1977. The measurement of observer agreement for categorical data. biometrics, pages 159–174. find
  42. Hugo Laurençon, Lucile Saulnier, Thomas Wang, Christopher Akiki, Albert Villanova del Moral, Teven Le Scao, Leandro Von Werra, Chenghao Mou, Eduardo González Ponferrada, Huu Nguyen, Jörg Frohberg, Mario Šaško, Quentin Lhoest, Angelina McMillan-Major, Gérard Dupont, Stella Biderman, Anna Rogers,… — The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
  43. Heather Lent, Emanuele Bugliarello, Miryam de Lhoneux, Chen Qiu, and Anders Søgaard. 2021. On language models for creoles. In Proceedings of the 25th Conference on Computational Natural Language Learning, pages 58–71, Online. Association for Computational Linguistics. find
  44. Zihan Liu, Genta Indra Winata, Zhaojiang Lin, Peng Xu, and Pascale Fung. 2020. Attention-informed mixed-language training for zero-shot cross-lingual task-oriented dialogue systems. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 8433–8440. find
  45. Niklas Muennighoff, Thomas Wang, Lintang Sutawika, Adam Roberts, Stella Biderman, Teven Le Scao, M Saiful Bari, Sheng Shen, Zheng-Xin Yong, Hailey Schoelkopf, Xiangru Tang, Dragomir Radev, Alham Fikri Aji, Khalid Almubarak, Samuel Albanie, Zaid Alyafeai, Albert Webson, Edward Raff, and Colin Raffel.… — Crosslingual Generalization through Multitask Finetuning
  46. Carol Myers-Scotton. 1997. Duelling languages: Grammatical structure in codeswitching. Oxford University Press. find
  47. Nathaniel Oco and Rachel Edita Roxas. 2012. Pattern matching refinements to dictionary-based code-switching point detection. In Proceedings of the 26th Pacific Asia Conference on Language, Information, and Computation, pages 229–236, Bali, Indonesia. Faculty of Computer Science, Universitas Indonesi… find
  48. Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instructions with human feedback. arXiv preprint arXiv:2203.02155.
  49. Femphy Pisceldo, Rahmad Mahendra, Ruli Manurung, and I Wayan Arka. 2008. A two-level morphological analyser for the Indonesian language. In Proceedings of the Australasian Language Technology Association Workshop 2008, pages 142–150, Hobart, Australia. find
  50. Holy Lovenia, Samuel Cahyawijaya, Genta Winata, Peng Xu, Yan Xu, Zihan Liu, Rita Frieske, Tiezheng Yu, Wenliang Dai, Elham J. Barezi, Qifeng Chen, Xiaojuan Ma, Bertram Shi, and Pascale Fung. 2022. ASCEND: A spontaneous Chinese-English dataset for code-switching in multi-turn conversation. In Proceed… find
  51. Shana Poplack. 1978. Syntactic structure and social function of code-switching, volume 2. Centro de Estudios Puertorriqueños,[City University of New York]. find
  52. Dau-Cheng Lyu, Tien-Ping Tan, Eng Siong Chng, and Haizhou Li. 2010. Seame: a mandarin-english code-switching speech corpus in south-east asia. In Eleventh Annual Conference of the International Speech Communication Association. find
  53. Adithya Pratapa, Gayatri Bhat, Monojit Choudhury, Sunayana Sitaram, Sandipan Dandapat, and Kalika Bali. 2018. Language modeling for code-mixing: The role of linguistic theory based synthetic data. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: L…
  54. Rawl Maliwat. 2021. Language policy and education in southeast asia. find
  55. Brian Migliazza. 1996. Mainland southeast asia: A unique linguistic area. Notes on linguistics, 75:17– 25. find
  56. Sneha Mondal, Ritika ., Shreya Pathak, Preethi Jyothi, and Aravindan Raghuveer. 2022. CoCoa: An encoder-decoder model for controllable code-switched generation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2466–2479, Abu Dhabi, United Arab Emirates… find
  57. Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, et al. 2023. Scaling speech technology to 1,000+ languages. arXiv preprint arXiv:2305.13516.
  58. Mont Redmond, Kimmo Kosonen, and Catherine Young. 2009. Mother tongue as bridge language of instruction: Policies and experiences in southeast Asia. World Bank. find
  59. Machel Reid, Victor Zhong, Suchin Gururangan, and Luke Zettlemoyer. 2022. M2D2: A massively multi-domain language modeling dataset. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 964–975, Abu Dhabi, United Arab Emirates. Association for Computational… find
  60. Mohd Sanad Zaki Rizvi, Anirudh Srinivasan, Tanuja Ganu, Monojit Choudhury, and Sunayana Sitaram. 2021. GCM: A toolkit for generating synthetic code-mixed text. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages… find
  61. Together Computer SambaNova Systems. 2023. BLOOMChat: a New Open Multilingual Chat LLM. find
  62. David Sankoff, Shana Poplack, and Swathi Vanniarajan. 1990. The case of the nonce loan in tamil. Language Variation and Change, 2(1):71–101. find
  63. Sebastin Santy, Anirudh Srinivasan, and Monojit Choudhury. 2021. BERTologiCoMix: How does code-mixing interact with multilingual BERT? In Proceedings of the Second Workshop on Domain Adaptation for NLP, pages 111–121, Kyiv, Ukraine. Association for Computational Linguistics. find
  64. Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al. 2022. Bloom: A 176b-parameter open-access multilingual language model. arXiv preprint arXiv:2211.05100. find
  65. Harold F. Schiffman. 1998. Malaysian tamils and tamil linguistic culture. find
  66. Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, and Ross Anderson. 2023. The curse of recursion: Training on generated data makes models forget. arXiv preprint arxiv:2305.17493.
  67. Yuen Sin. 2017. Don’t play, play - singlish is studied around the globe. find
  68. Department of Statistics Singapore. 2020. Key findings 2020. find
  69. James Neil Sneddon. 2003. The Indonesian language: Its history and role in modern society. UNSW Press, Sydney. find
  70. Thamar Solorio and Yang Liu. 2008. Learning to predict code-switching points. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pages 973–981, Honolulu, Hawaii. Association for Computational Linguistics.
  71. Samson Tan and Shafiq Joty. 2021. Code-mixing on sesame street: Dawn of the adversarial polyglots. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3596–3616, Online. Association for Computational… find
  72. Ruixiang Tang, Xiaotian Han, Xiaoqian Jiang, and Xia Hu. 2023. Does synthetic data generation of llms help clinical text mining? arXiv preprint arXiv:2303.04360. — [arXiv:2303.04360](https://arxiv.org/abs/2303.04360) find
  73. Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Stanford alpaca: An instruction-following llama model. https://github.com/tatsu-lab/stanford_alpaca. ↗
  74. Rohan Taori and Tatsunori B Hashimoto. 2022. Data feedback loops: Model-driven amplification of dataset biases. arXiv preprint arXiv:2209.03942. — [arXiv:2209.03942](https://arxiv.org/abs/2209.03942) find
  75. Ishan Tarunesh, Syamantak Kumar, and Preethi Jyothi. 2021. From machine translation to code-switching: Generating high-quality code-switched text. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Languag… find
  76. C Tho, Y Heryadi, L Lukas, and A Wibowo. 2021. Code-mixed sentiment analysis of indonesian language and javanese language using lexicon based approach. Journal of Physics: Conference Series, 1869(1):012084. find
  77. Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al. 2022. Lamda: Language models for dialog applications. arXiv preprint arXiv:2201.08239.
  78. A. J. Toribio. 2002. Spanish-english code-switching among us latinos. International Journal of the Sociology of Languaeg, 2002:89–119. find
  79. Chenxi Whitehouse, Monojit Choudhury, and Alham Fikri Aji. 2023. Llm-powered data augmentation for enhanced crosslingual performance. arXiv preprint arXiv:2305.14288. — [arXiv:2305.14288](https://arxiv.org/abs/2305.14288) find
  80. Genta Indra Winata, Alham Fikri Aji, Samuel Cahyawijaya, Rahmad Mahendra, Fajri Koto, Ade Romadhony, Kemal Kurniawan, David Moeljadi, Radityo Eko Prasojo, Pascale Fung, Timothy Baldwin, Jey Han Lau, Rico Sennrich, and Sebastian Ruder. 2023. NusaX: Multilingual parallel sentiment dataset for 10 Indon…
  81. Genta Indra Winata, Alham Fikri Aji, Zheng-Xin Yong, and Thamar Solorio. 2022. The decades progress on code-switching research in nlp: A systematic survey on trends and challenges. arXiv preprint arXiv:2212.09660. find
  82. Genta Indra Winata, Samuel Cahyawijaya, Zihan Liu, Zhaojiang Lin, Andrea Madotto, and Pascale Fung. 2021. Are multilingual models effective in code-switching? In Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching, pages 142–153, Online. Association for Computa… find
  83. Genta Indra Winata, Samuel Cahyawijaya, Zihan Liu, Zhaojiang Lin, Andrea Madotto, Peng Xu, and Pascale Fung. 2020. Learning fast adaptation on cross-accented speech recognition. In Interspeech 2020. ISCA. find
  84. Genta Indra Winata, Andrea Madotto, Chien-Sheng Wu, and Pascale Fung. 2019. Code-switched language models using neural based synthetic data from parallel sentences. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 271–280. find
  85. Evi Yulianti, Ajmal Kurnia, Mirna Adriani, and Yoppy Setyo Duto. 2021. Normalisation of indonesian-english code-mixed text and its effect on emotion classification. International Journal of Advanced Computer Science and Applications, 12(11). find
  86. Ruochen Zhang, Samuel Cahyawijaya, Jan Christian Blaise Cruz, and Alham Fikri Aji. 2023. Multilingual large language models are not (yet) code-switchers.
  87. Ruochen Zhang and Carsten Eickhoff. 2023. Crocosum: A benchmark dataset for cross-lingual code-switched summarization. arXiv preprint arXiv:2303.04092. find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/yong-2023-prompting-multilingual-large-language-models/