參考文獻 |
[1] OpenAI, “Gpt-4 technical report,” ArXiv, vol. abs/2303.08774, 2023. [Online].
Available: https://api.semanticscholar.org/CorpusID:257532815
[2] H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Roz-
ière, N. Goyal, E. Hambro, F. Azhar, A. Rodriguez, A. Joulin, E. Grave, and G. Lam-
ple, “Llama: Open and efficient foundation language models,” 2023.
[3] H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov,
S. Batra, P. Bhargava, S. Bhosale et al., “Llama 2: Open foundation and fine-tuned
chat models,” arXiv preprint arXiv:2307.09288, 2023.
[4] Y. Cui, Z. Yang, and X. Yao, “Efficient and effective text encoding for chinese
llama and alpaca,” arXiv preprint arXiv:2304.08177, 2023. [Online]. Available:
https://arxiv.org/abs/2304.08177
[5] A. Balachandran, “Tamil-llama: A new tamil language model based on llama 2,”
2023.
[6] P. S. Ding, Introduction. Singapore: Springer Singapore, 2016, pp. 1–18. [Online].
Available: https://doi.org/10.1007/978-981-287-594-5_1
[7] ——, Taiwan: The Haven for Southern Min? Singapore: Springer Singapore, 2016,
pp. 55–75. [Online]. Available: https://doi.org/10.1007/978-981-287-594-5_4
[8] Y.-F. Liao, C.-Y. Chang, H.-K. Tiun, H.-L. Su, H.-L. Khoo, J. S. Tsay,
L.-K. Tan, P. Kang, T.-g. Thiann, U.-G. Iunn, J.-H. Yang, and C.-N. Liang,
“Formosa Speech Recognition Challenge 2020 and Taiwanese Across Taiwan
Corpus,” in 2020 23rd Conference of the Oriental COCOSDA International
Committee for the Co-ordination and Standardisation of Speech Databases and
Assessment Techniques (O-COCOSDA), 2020, pp. 65–70. [Online]. Available:
https://ieeexplore.ieee.org/document/9295019
[9] Y. Moslem, R. Haque, J. D. Kelleher, and A. Way, “Adaptive Machine
Translation with Large Language Models,” in Proceedings of the 24th Annual
Conference of the European Association for Machine Translation. European
Association for Machine Translation, 2023, pp. 227–237. [Online]. Available:
https://aclanthology.org/2023.eamt-1.22
[10] X. V. Lin, T. Mihaylov, M. Artetxe, T. Wang, S. Chen, D. Simig, M. Ott,
N. Goyal, S. Bhosale, J. Du, R. Pasunuru, S. Shleifer, P. S. Koura, V. Chaudhary,
B. O’Horo, J. Wang, L. Zettlemoyer, Z. Kozareva, M. Diab, V. Stoyanov, and
X. Li, “Few-shot Learning with Multilingual Generative Language Models,” in
Proceedings of the 2022 Conference on Empirical Methods in Natural Language
Processing. Association for Computational Linguistics, 2022, pp. 9019–9052.
[Online]. Available: https://aclanthology.org/2022.emnlp-main.616
[11] W. Zhu, H. Liu, Q. Dong, J. Xu, L. Kong, J. Chen, L. Li, and S. Huang, “Multilingual
machine translation with large language models: Empirical results and analysis,”
arXiv preprint arXiv:2304.04675, 2023.
[12] B. Zhang, B. Haddow, and A. Birch, “Prompting large language model for machine
translation: A case study,” 2023.
[13] D. Vilar, M. Freitag, C. Cherry, J. Luo, V. Ratnakar, and G. Foster, “Prompting
PaLM for Translation: Assessing Strategies and Performance,” in Proceedings
of the 61st Annual Meeting of the Association for Computational Linguistics
(Volume 1: Long Papers). Association for Computational Linguistics, 2023, pp.
15 406–15 427. [Online]. Available: https://aclanthology.org/2023.acl-long.859
[14] X. García, Y. Bansal, C. Cherry, G. F. Foster, M. Krikun, F. Feng, M. Johnson,
and O. Firat, “The unreasonable effectiveness of few-shot learning for machine
translation,” ArXiv, vol. abs/2302.01398, 2023. [Online]. Available: https:
//api.semanticscholar.org/CorpusID:256598283
[15] T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal,
A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-
Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu,
C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess,
J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei,
“Language models are few-shot learners,” in Advances in Neural Information
Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan,
and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 1877–
1901. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2020/
file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
[16] S. Zhang, Q. Fang, Z. Zhang, Z. Ma, Y. Zhou, L. Huang, M. Bu, S. Gui, Y. Chen,
X. Chen et al., “Bayling: Bridging cross-lingual alignment and instruction fol-
lowing through interactive translation for large language models,” arXiv preprint
arXiv:2306.10968, 2023.
[17] W. Yang, C. Li, J. Zhang, and C. Zong, “Bigtrans: Augmenting large language
models with multilingual translation capability over 100 languages,” arXiv preprint
arXiv:2305.18098, 2023.
[18] J. Li, H. Zhou, S. Huang, S. Chen, and J. Chen, “Eliciting the translation
ability of large language models via multilingual finetuning with translation
instructions,” ArXiv, vol. abs/2305.15083, 2023. [Online]. Available: https:
//api.semanticscholar.org/CorpusID:258865882
[19] L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang,
S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller,
M. Simens, A. Askell, P. Welinder, P. Christiano, J. Leike, and R. Lowe, “Train-
ing language models to follow instructions with human feedback,” 2022.
[20] A. Hendy, M. G. Abdelrehim, A. Sharaf, V. Raunak, M. Gabr, H. Matsushita,
Y. J. Kim, M. Afify, and H. H. Awadalla, “How good are gpt models at machine
translation? a comprehensive evaluation,” ArXiv, vol. abs/2302.09210, 2023.
[Online]. Available: https://api.semanticscholar.org/CorpusID:257038384
[21] W. Jiao, W. Wang, J. Huang, X. Wang, and Z. Tu, “Is chatgpt a good translator? yes
with gpt-4 as the engine,” arXiv preprint arXiv:2301.08745, 2023.
[22] H. Xu, Y. J. Kim, A. Sharaf, and H. H. Awadalla, “A paradigm shift in machine trans-
lation: Boosting translation performance of large language models,” arXiv preprint
arXiv:2309.11674, 2023.
[23] N. Team, M. R. Costa-jussà, J. Cross, O. Çelebi, M. Elbayad, K. Heafield, K. Hef-
fernan, E. Kalbassi, J. Lam, D. Licht, J. Maillard, A. Sun, S. Wang, G. Wen-
zek, A. Youngblood, B. Akula, L. Barrault, G. M. Gonzalez, P. Hansanti, J. Hoff-
man, S. Jarrett, K. R. Sadagopan, D. Rowe, S. Spruit, C. Tran, P. Andrews, N. F.
Ayan, S. Bhosale, S. Edunov, A. Fan, C. Gao, V. Goswami, F. Guzmán, P. Koehn,
A. Mourachko, C. Ropers, S. Saleem, H. Schwenk, and J. Wang, “No language left
behind: Scaling human-centered machine translation,” 2022.
[24] Y.-F. Liao, J. S. Tsay, P. Kang, H.-L. Khoo, L.-K. Tan, L.-C. Chang, U.-G. Iunn, H.-
L. Su, T.-G. Thiann, H.-K. Tiun, and S.-L. Liao, “Taiwanese across taiwan corpus
and its applications,” in 2022 25th Conference of the Oriental COCOSDA Interna-
tional Committee for the Co-ordination and Standardisation of Speech Databases
and Assessment Techniques (O-COCOSDA), 2022, pp. 1–5.
[25] S.-E. Lu, B.-H. Lu, C.-Y. Lu, and R. T.-H. Tsai, “Exploring methods for building
dialects-Mandarin code-mixing corpora: A case study in Taiwanese hokkien,” in
Findings of the Association for Computational Linguistics: EMNLP 2022. Abu
Dhabi, United Arab Emirates: Association for Computational Linguistics, Dec.
2022, pp. 6287–6305. [Online]. Available: https://aclanthology.org/2022.findings-
emnlp.469
[26] A. Conneau and G. Lample, “Cross-lingual language model pretraining,” Advances
in neural information processing systems, vol. 32, 2019.
[27] T. Kudo and J. Richardson, “SentencePiece: A simple and language independent
subword tokenizer and detokenizer for neural text processing,” in Proceedings of the
2018 Conference on Empirical Methods in Natural Language Processing: System
Demonstrations. Brussels, Belgium: Association for Computational Linguistics,
Nov. 2018, pp. 66–71. [Online]. Available: https://aclanthology.org/D18-2012
[28] E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen,
“Lora: Low-rank adaptation of large language models,” 2021.
[29] K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic
evaluation of machine translation,” in Proceedings of the 40th Annual Meeting
of the Association for Computational Linguistics. Philadelphia, Pennsylvania,
USA: Association for Computational Linguistics, Jul. 2002, pp. 311–318. [Online].
Available: https://aclanthology.org/P02-1040
[30] M. Popović, “chrF++: words helping character n-grams,” in Proceedings of the
Second Conference on Machine Translation. Copenhagen, Denmark: Association
for Computational Linguistics, Sep. 2017, pp. 612–618. [Online]. Available:
https://aclanthology.org/W17-4770
[31] T. Kocmi and C. Federmann, “Large language models are state-of-the-art evaluators
of translation quality,” in Proceedings of the 24th Annual Conference of the
European Association for Machine Translation. Tampere, Finland: European
Association for Machine Translation, Jun. 2023, pp. 193–203. [Online]. Available:
https://aclanthology.org/2023.eamt-1.19
[32] W. Zhu, Y. Lv, Q. Dong, F. Yuan, J. Xu, S. Huang, L. Kong, J. Chen, and L. Li,
“Extrapolating large language models to non-english by aligning languages,” 2023.
[33] M. Conover, M. Hayes, A. Mathur, J. Xie, J. Wan, S. Shah, A. Ghodsi, P. Wendell,
M. Zaharia, and R. Xin. (2023) Free dolly: Introducing the world’s first truly open
instruction-tuned llm. [Online]. Available: https://www.databricks.com/blog/2023/
04/12/dolly-first-open-commercially-viable-instruction-tuned-llm
[34] R. Taori, I. Gulrajani, T. Zhang, Y. Dubois, X. Li, C. Guestrin, P. Liang, and
T. B. Hashimoto, “Stanford alpaca: An instruction-following llama model,” https:
//github.com/tatsu-lab/stanford_alpaca, 2023.
[35] Y.-C. Huang, Y.-L. Hsieh, Y.-Y. Lin, T. L. Hui, H.-Y. Chu, and W.-L. Hsu. (2021)
FLUD: Expert-curated large-scale machine comprehension dataset with advanced
reasoning strategies. [Online]. Available: https://www.kistep.re.kr/arpIssue.es?act=
content_view&list_no=200&act=content_view&mid=a20802000000 |