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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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Efficient Zero-Shot Cross-lingual Inference via Retrieval
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Kulikov, V., Neychev, R., & Makarov, I. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-67008-4_4 · Cited by 1
PsychoLex: Unveiling the Psychological Mind of Large Language ModelsPreprint
Abbasi, M. A., Mirnezami, F. S., & Naderi, H. (2024) · Research Square · doi:10.21203/rs.3.rs-4920871/v1 · Cited by 1
Self-Pluralising Culture Alignment for Large Language ModelsPreprint
Xu, S., Leng, Y., Yu, L., & Xiong, D. (2024) · Qeios · doi:10.32388/q7ywfv · Cited by 1
A Data-Centric Perspective on the Lifecycle of Large Language ModelsPreprint
Rao, J., Liu, X., Yan, H., Shen, J., Mo, H., Dong, Y., Yan, Z., Wang, Z., Lin, Z., Meng, X., et al. (2025) · doi:10.36227/techrxiv.176620610.03288677/v1 · Cited by 2
World Wide Models: Literary Tools for Cultural AIPreprint
Beguš, N. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.5865344 · Cited by 1
Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale
Koepke, A. S., Zverev, D., Ginosar, S., & Efros, A. A. (2026) · arXiv · doi:10.48550/arxiv.2604.18572
“We follow the original experimental setup from [42] to evaluate 55 LLMs, spanning from BLOOMZ [71] to recently released models.”
da Silva, C. I., Filho, J. L. S., de Almeida, J., da Silva, D. R. B., Stalbaum, B., & Borges, M. D. A. (2023) · Iberian Conference on Information Systems and Technologies (CISTI) · doi:10.23919/cisti58278.2023.10211462
Arabic Topic Classification in the Generative and AutoML Era
Albared, D., Hamoud, H. K., & Zaraket, F. A. (2023) · Arabic Natural Language Processing Conference (ArabicNLP) · doi:10.18653/v1/2023.arabicnlp-1.32
Findings of the 1st Shared Task on Multi-lingual Multi-task Information Retrieval at MRL 2023
Tinner, F., Adelani, D. I., Emezue, C. C., Hajili, M., Goldman, O., Adilazuarda, M. F., Kautsar, M. D. A., Mirsaidova, A., Kural, M., Klenner, D. M. M., et al. (2023) · Workshop on Multilingual Representation Learning (MRL) · doi:10.18653/v1/2023.mrl-1.24
Measuring bias in Instruction-Following models with P-AT
Onorati, D., Ruzzetti, E. S., Venditti, D., Ranaldi, L., & Zanzotto, F. M. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.539
Computer-assisted detection of typologically relevant semantic shifts in world languages
Gruntov, I., & Elisei, R. (2023) · Computational Linguistics and Intellectual Technologies · doi:10.28995/2075-7182-2023-22-161-171
Generation of Korean Offensive Language by Leveraging Large Language Models via Prompt Design
Shin, J., Song, H., Lee, H., Gaim, F., & Park, J. C. (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.62 · Cited by 1
Evaluating Large Language Models’ Understanding of Financial Terminology via Definition Modeling
Jhirad, J., Marrese-Taylor, E., & Matsuo, Y. (2023) · doi:10.18653/v1/2023.ijcnlp-srw.12
Mayhew, S., Blevins, T., Plank, B., Karlsson, B. F., Deng, C., Artemova, E., Aco, E., Stenström, E., Jazbec, I.-P., Rhald Caballes Padilla, J., et al. (2023) · HAL · doi:10.7910/dvn/gq8hdl
Puspo, S. S. C., Raihan, N., Goswami, D., Emran, A. N. B., Ganguly, A., & Uzuner, Ö. (2024) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2024.semeval-1.197
HW-TSC 2024 Submission for the SemEval-2024 Task 1: Semantic Textual Relatedness (STR)
Piao, M., Su, C., Li, Y., Qiao, X., Zhao, X., Li, Y., Zhang, M., & Yang, H. (2024) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2024.semeval-1.232
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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