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This paper presents the outcomes of fine-tuning Mistral 7B, a general-purpose large language model (LLM), for adaptive machine translation (MT).
Language Models are Few-Shot Learners
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ELRC White Paper: Sustainable Language Data Sharing to Support Language Equality in Multilingual Europe: why Language Data Matters
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Bai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., Fan, Y., Ge, W., Han, Y., Huang, F., Hui, B., Ji, L., Li, M., Lin, J., Lin, R., Liu, D., Liu, G., Lu, C., Lu, K., Ma, J., Men, R., Ren, X., Ren, X., Tan, C., Tan, S., Tu, J., Wang, P., Wang, S., Wang, W., Wu, S., Xu, B., Xu, J., Yang, A., Yang, H., Yang, J., Yang, S., Yao, Y., Yu, B., Yuan, H., Yuan, Z., Zhang, J., Zhang, X., Zhang, Y., Zhang, Z., Zhou, C., Zhou, J., Zhou, X., and Zhu, T. (2023) · 2023
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No Language Left Behind: Scaling human-centered machine translation
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BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
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Neural Machine Translation Models Can Learn to be Few-shot Learners
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Sengupta, N., Sahu, S. K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O. M., Kamboj, S., Pandit, O., Pal, R., Pradhan, L., Mujahid, Z. M., Baali, M., Aji, A. F., Liu, Z., Hock, A., Feldman, A., Lee, J., Jackson, A., Nakov, P., Baldwin, T., and Xing, E. (2023) · 2023
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Yang, A., Xiao, B., Wang, B., Zhang, B., Bian, C., Yin, C., Lv, C., Pan, D., Wang, D., Yan, D., Yang, F., Deng, F., Wang, F., Liu, F., Ai, G., Dong, G., Zhao, H., Xu, H., Sun, H., Zhang, H., Liu, H., Ji, J., Xie, J., Dai, J., Fang, K., Su, L., Song, L., Liu, L., Ru, L., Ma, L., Wang, M., Liu, M., Lin, M., Nie, N., Guo, P., Sun, R., Zhang, T., Li, T., Li, T., Cheng, W., Chen, W., Zeng, X., Wang, X., Chen, X., Men, X., Yu, X., Pan, X., Shen, Y., Wang, Y., Li, Y., Jiang, Y., Gao, Y., Zhang, Y., Zhou, Z., and Wu, Z. (2023) · 2023
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Machine Translation with Large Language Models: Prompting, Few-shot Learning, and Fine-tuning with QLoRA
Zhang, X., Rajabi, N., Duh, K., and Koehn, P. (2023) · 2023
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