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Crosslingual Generalization through Multitask Finetuning
Open accessAbstract
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, Colin Raffel. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
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“The scientific documentation of BloomZ also includes multiple peer-reviewed papers , from a scientific description of the multitask fine-tuning procedures [41] to an estimation of the carbon footprint [36] currently one of the very few scientifically vetted sources of data on the energy footprint of training large language models. No peerreviewed papers providing scientific documentation or eval”
From Rethinking open source generative AI: open washing and the EU AI Act (2024) · cites this paper as [41]
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“We try to avoid using machine-translated data to finetune the model in order to prevent translationese (Bizzoni et al., 2020; Muennighoff et al., 2023) or possible name bias (Wang et al., 2022a), gender bias (Savoldi et al., 2021), or cultural bias (Ji et al., 2023).”
From The Llama 3 Herd of Models (2024) · cites this paper as Muennighoff et al., 2023
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TARJAMAT: Evaluation of Bard and ChatGPT on Machine Translation of Ten Arabic Varieties
Kadaoui, K., Magdy, S. M., Waheed, A., Khondaker, M. T. I., El-Shangiti, A. O., Nagoudi, E. M. B., & Abdul-Mageed, M. (2023) · Arabic Natural Language Processing Conference (ArabicNLP) · doi:10.18653/v1/2023.arabicnlp-1.6 · Cited by 46
LLMs-based machine translation for E-commerce
Gao, D., Chen, K., Chen, B., Dai, H.-F., Jin, L., Jiang, W., Ning, W., Yu, S., Xuan, Q., Cai, X., et al. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125087 · Cited by 44
Nguyen, X.-P., Aljunied, M., Joty, S., & Bing, L. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-long.192 · Cited by 61
Choi, M., Pei, J., Kumar, S., Shu, C., & Jurgens, D. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.699 · Cited by 38
EcomGPT: Instruction-Tuning Large Language Models with Chain-of-Task Tasks for E-commerce
Li, Y., Ma, S., Wang, X., Huang, S., Jiang, C., Zheng, H.-T., Xie, P., Huang, F., & Jiang, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29820 · Cited by 58
Chen, L., Zuo, H., Cai, Z., Yin, Y., Zhang, Y. E., Sun, L., Childs, P., & Wang, B. (2024) · Journal of Mechanical Design · doi:10.1115/1.4065562 · Cited by 41
Wu, Z., Zhang, L., Cao, C., Yu, X., Liu, Z., Zhao, L., Li, Y., Dai, H., Ma, C., Li, G., et al. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3536928 · Cited by 56
Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon
Koto, F., Beck, T., Talat, Z., Gurevych, I., & Baldwin, T. (2024) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2024.eacl-long.18 · Cited by 36
NADI 2023: The Fourth Nuanced Arabic Dialect Identification Shared Task
Abdul-Mageed, M., Elmadany, A., Zhang, C., Nagoudi, E. M. B., Bouamor, H., & Habash, N. Y. (2023) · Arabic Natural Language Processing Conference (ArabicNLP) · doi:10.18653/v1/2023.arabicnlp-1.62 · Cited by 35
Jain, R., Sojitra, D., Acharya, A., Saha, S., Jatowt, A., & Dandapat, S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.418 · Cited by 39
BLESS: Benchmarking Large Language Models on Sentence Simplification
Kew, T., Chi, A., Vásquez-Rodríguez, L., Agrawal, S., Aumiller, D., Alva-Manchego, F. E., & Shardlow, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.821 · Cited by 50
SLM: Bridge the Thin Gap Between Speech and Text Foundation Models
Wang, M., Han, W., Izhak Shafran, J., Wu, Z., Chiu, C., Cao, Y. J., Chen, N., Zhang, Y., Soltau, H., Rubenstein, P. K., et al. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389703 · Cited by 47
Beyond Words: A Comparative Analysis of LLM Embeddings for Effective Clustering
Keraghel, I., Morbieu, S., & Nadif, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-58547-0_17 · Cited by 40
BiMediX: Bilingual Medical Mixture of Experts LLM
Pieri, S., Mullappilly, S. S., Khan, F. S., Anwer, R. M., Khan, S., Baldwin, T., & Cholakkal, H. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.989 · Cited by 52
Artificial general intelligence for radiation oncology
Liu, C., Liu, Z., Holmes, J., Zhang, L., Zhang, L., Ding, Y., Shu, P., Wu, Z., Dai, H., Li, Y., et al. (2023) · Meta-Radiology · doi:10.1016/j.metrad.2023.100045 · Cited by 39
Yong, Z., Zhang, R., Forde, J. Z., Wang, S., Subramonian, A., Lovenia, H., Cahyawijaya, S., Winata, G. I., Sutawika, L., Cruz, J. C. B., et al. (2023) · Workshop on Computational Approaches to Code Switching · doi:10.18653/v1/2023.calcs-1.5 · Cited by 48
Self-citationEvaluating Diverse Large Language Models for Automatic and General Bug Reproduction
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MasakhaNEWS: News Topic Classification for African languages
Adelani, D. I., Masiak, M., Azime, I. A., Alabi, J. O., Tonja, A. L., Mwase, C., Ogundepo, O., Dossou, B. F. P., Oladipo, A., Nixdorf, D., et al. (2023) · International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) · doi:10.18653/v1/2023.ijcnlp-main.10 · Cited by 46
Lai, V. D., Nguyen, C., Ngo, N., Nguyen, T., Dernoncourt, F., Rossi, R. A., & Nguyen, T. H. (2023) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2023.emnlp-demo.28 · Cited by 51
TR2MTL: LLM based framework for Metric Temporal Logic Formalization of Traffic Rules
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To What Extent Have LLMs Reshaped the Legal Domain So Far? A Scoping Literature Review
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Multilingual Large Language Models Are Not (Yet) Code-Switchers
Zhang, R., Cahyawijaya, S., Cruz, J. C. B., Winata, G. I., & Aji, A. F. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.774 · Cited by 55
Self-citationXCOT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning
Chai, L., Yang, J. J., Sun, T., Guo, H., Liu, J., Wang, B., Liang, X., Bai, J., Li, T., Peng, Q., et al. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i22.34524 · Cited by 30
Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Models
Qi, J., Fernández, R., & Bisazza, A. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.658 · Cited by 59
Enhancing Zero-Shot Crypto Sentiment With Fine-Tuned Language Model and Prompt Engineering
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Metadata
- Authors
- 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., & Raffel, C.
- Affiliations
- University of Cambridge (GB)
- Google (United States) (US)
- Booz Allen Hamilton (United States) (US)
- University of California, Berkeley (US)
- Brown University (US)
- King Fahd University of Petroleum and Minerals (SA)
- Corresponding author
- Muennighoff, Niklas
- ORCID
- Venue
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2023
- Pages
- 15991-16111
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4385571124
- Cited by
- 361
- References
- 33
- Citations by year
- 2026: 57 · 2025: 76 · 2024: 112 · 2023: 116
- FWCI
- 63.96
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Funders
- Centre National de la Recherche Scientifique
- Grand Équipement National De Calcul Intensif
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- cross-lingual generalization, multilingual language models, transfer learning, low-resource languages
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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