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Multilingual large language models (MLLMs) have demonstrated significant cross-lingual capabilities through in-context learning.
Roberta: A robustly optimized bert pretraining approach, 2019
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 1907
Earlier work this paper cites.
Scaling laws for neural language models, 2020
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D · 2001
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J · 2002
Earlier work this paper cites.
SQuAD: 100,000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P · 2016
Earlier work this paper cites.
Xnli: Evaluating cross-lingual sentence representations
Conneau, A., Rinott, R., Lample, G., Williams, A., Bowman, S., Schwenk, H., and Stoyanov, V · 2018
Earlier work this paper cites.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I · 2019
Earlier work this paper cites.
NCLS: Neural cross-lingual summarization
Zhu, J., Wang, Q., Wang, Y., Zhou, Y., Zhang, J., Wang, S., and Zong, C · 2019
Earlier work this paper cites.
On the cross-lingual transferability of monolingual representations
Artetxe, M., Ruder, S., and Yogatama, D · 2020
Earlier work this paper cites.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
Earlier work this paper cites.
Unsupervised cross-lingual representation learning at scale
Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., and Stoyanov, V · 2020
Cited alongside, same era.
XTREME: A massively multilingual multi-task benchmark for evaluating cross-lingual generalisation
Hu, J., Ruder, S., Siddhant, A., Neubig, G., Firat, O., and Johnson, M · 2020
Cited alongside, same era.
MLQA: Evaluating cross-lingual extractive question answering
Lewis, P., Oguz, B., Rinott, R., Riedel, S., and Schwenk, H · 2020
Cited alongside, same era.
Xcopa: A multilingual dataset for causal commonsense reasoning
Ponti, E. M., Glavaš, G., Majewska, O., Liu, Q., Vulić, I., and Korhonen, A · 2020
Cited alongside, same era.
Language models are few-shot multilingual learners
Winata, G. I., Madotto, A., Lin, Z., Liu, R., Yosinski, J., and Fung, P · 2021
Cited alongside, same era.
mT5: A massively multilingual pre-trained text-to-text transformer
Bloom: A 176b-parameter open-access multilingual language model, 2022
Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Ilić, S., Hesslow, D., Castagné, R., Luccioni, A. S., Yvon, F., Gallé, M., Tow, J., and Rush, Alexander M., e. a · 2022
Later among the works it cites.
MEGA: Multilingual evaluation of generative AI
Ahuja, K., Diddee, H., Hada, R., Ochieng, M., Ramesh, K., Jain, P., Nambi, A., Ganu, T., Segal, S., Ahmed, M., Bali, K., and Sitaram, S · 2023
Closest in time.
Anil, R., Dai, A. M., Firat, O., Johnson, M., Lepikhin, D., Passos, A., Shakeri, S., Taropa, E., Bailey, P., Chen, Z., et al · 2023
Closest in time.
Buffet: Benchmarking large language models for few-shot cross-lingual transfer
Asai, A., Kudugunta, S., Yu, X. V., Blevins, T., Gonen, H., Reid, M., Tsvetkov, Y., Ruder, S., and Hajishirzi, H · 2023
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Xue, L., Constant, N., Roberts, A., Kale, M., Al-Rfou, R., Siddhant, A., Barua, A., and Raffel, C · 2021
Cited alongside, same era.
The bigscience ROOTS corpus: A 1.6TB composite multilingual dataset
Laurençon, H., Saulnier, L., Wang, T., Akiki, C., del Moral, A. V., Scao, T. L., Werra, L. V., Mou, C., Ponferrada, E. G., Nguyen, H., Frohberg, J., Šaško, M., Lhoest, Q., McMillan-Major, A., Dupont, G., Biderman, S., Rogers, A., allal, L. B., Toni, F. D., Pistilli, G., Nguyen, O., Nikpoor, S., Masoud, M., Colombo, P., de la Rosa, J., Villegas, P., Thrush, T., Longpre, S., Nagel, S., Weber, L., Muñoz, M. R., Zhu, J., Strien, D. V., Alyafeai, Z., Almubarak, K., Chien, V. M., Gonzalez-Dios, I., Soroa, A., Lo, K., Dey, M., Suarez, P. O., Gokaslan, A., Bose, S., Adelani, D. I., Phan, L., Tran, H., Yu, I., Pai, S., Chim, J., Lepercq, V., Ilic, S., Mitchell, M., Luccioni, S., and Jernite, Y · 2022
Cited alongside, same era.
Few-shot learning with multilingual generative language models
Lin, X. V., Mihaylov, T., Artetxe, M., Wang, T., Chen, S., Simig, D., Ott, M., Goyal, N., Bhosale, S., Du, J., Pasunuru, R., Shleifer, S., Koura, P. S., Chaudhary, V., O’Horo, B., Wang, J., Zettlemoyer, L., Kozareva, Z., Diab, M., Stoyanov, V., and Li, X · 2022
Cited alongside, same era.
Huang, H., Tang, T., Zhang, D., Zhao, W. X., Song, T., Xia, Y., and Wei, F · 2023
Closest in time.
Chatgpt beyond english: Towards a comprehensive evaluation of large language models in multilingual learning, 2023
Lai, V. D., Ngo, N. T., Veyseh, A. P. B., Man, H., Dernoncourt, F., Bui, T., and Nguyen, T. H · 2023
Closest in time.
Crosslingual generalization through multitask finetuning
Muennighoff, N., Wang, T., Sutawika, L., Roberts, A., Biderman, S., Le Scao, T., Bari, M. S., Shen, S., Yong, Z. X., Schoelkopf, H., Tang, X., Radev, D., Aji, A. F., Almubarak, K., Albanie, S., Alyafeai, Z., Webson, A., Raff, E., and Raffel, C · 2023
Closest in time.
Language models are multilingual chain-of-thought reasoners
Shi, F., Suzgun, M., Freitag, M., Wang, X., Srivats, S., Vosoughi, S., Chung, H. W., Tay, Y., Ruder, S., Zhou, D., Das, D., and Wei, J · 2023
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