Elamur Research⌕
☰
home / papers / 2025 / Analyzing and Adapting Large Language Models for Few-Shot Multilingual NLU: Are We There Yet?

Analyzing and Adapting Large Language Models for Few-Shot Multilingual NLU: Are We There Yet?

Paper · 2025 · Evgeniia Razumovskaia, Ivan Vulić and Anna Korhonen

Summary

Few-shot adaptation of NLP models to new languages can follow three paths: supervised fine-tuning (SFT) of compact multilingual models, supervised instruction tuning (SIT), or in-context learning (ICL) with large generative LLMs. Earlier comparisons mostly ranked these options by accuracy alone and covered only small groups of languages. This TACL 2025 study from Cambridge's Language Technology Lab gives all three approaches the same annotated examples. It tests them on five token- and sentence-level NLU classification tasks, across high- and low-resource languages and several language and domain configurations. Beyond task scores, it also weighs data needs, compute and memory, inference latency and monetary cost. Supervised training of smaller models such as mT0 beats ICL and is cheaper to run. SIT offers the best balance of quality and expense. The authors also check whether adapting English-dominant LLMs (Llama-2, MaLA-500) to a target language improves ICL. The generated text becomes more fluent, but language understanding barely improves, especially for low-resource languages. For practitioners working outside English, compact supervised multilingual models remain the practical option.

Key points

  • On intent detection from Multi3NLU++ (Amharic and Spanish, identical data), ICL had lower accuracy and higher latency and compute demands than SFT/SIT. Its only advantage was a smaller financial cost.
  • Llama-2's pretraining data is roughly 90% English, which the authors link to its weak performance in other languages.
  • In a Spanish human evaluation after language adaptation, average naturalness rose from 1.4 to 2.2 on a 3-point scale. Usefulness rose only from 1.4 to 1.6.
  • On XNLI, ICL accuracy after adaptation went from 30.5 to 30.7. This change is not significant and remains below the 33% random baseline. Models often ignored instructions and output formats.
  • MaLA-500 is a Llama-2 model adapted to 534 languages on Glot-500-c, using LoRA and vocabulary extension. On in-language in-domain intent detection it scored only about 0–3, far behind GPT-3.5 ICL and SIT with mT0-Base trained on 100 examples. A translate-test pipeline with GPT-3.5 helped Amharic, but SIT still won on the other languages.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of CambridgeCambridge · United Kingdom243 works · 121,396 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksMultilingual & cross-lingual NLP · 828 works

Cite

Cite as Evgeniia Razumovskaia, Ivan Vulić and Anna Korhonen. 2025. Analyzing and Adapting Large Language Models for Few-Shot Multilingual NLU: Are We There Yet?. Transactions of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/tacl.a.33

@article{razumovskaia2025analyzing,
  title = {{Analyzing and Adapting Large Language Models for Few-Shot Multilingual NLU: Are We There Yet?}},
  author = {Evgeniia Razumovskaia, Ivan Vulić and Anna Korhonen},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2025},
  doi = {10.1162/tacl.a.33},
  url = {https://doi.org/10.1162/tacl.a.33}
}

References

76 references, 40 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. ArXiv preprint, abs/2303.08774.
  2. David Ifeoluwa Adelani, Jade Abbott, Graham Neubig, Daniel D’souza, Julia Kreutzer, Constantine Lignos, Chester Palen-Michel, Happy Buzaaba, Shruti Rijhwani, Sebastian Ruder, et al. 2021. Masakhaner: Named entity recognition for african languages. Transactions of the Association for Computational Li…
  3. Keivan Alizadeh, Iman Mirzadeh, Dmitry Belenko, Karen Khatamifard, Minsik Cho, Carlo C. Del Mundo, Mohammad Rastegari, and Mehrdad Farajtabar. 2023. Llm in a flash: Efficient large language model inference with limited memory. ArXiv preprint, abs /2312.11514.
  4. Reza Yazdani Aminabadi, Samyam Rajbhandari, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Olatunji Ruwase, Shaden Smith, Minjia Zhang, Jeff Rasley, et al. 2022. Deepspeed-inference: Enabling efficient inference of transformer models at unprecedented scale. In SC22: International Conference for Hig… find
  5. Alan Ansell, Edoardo Maria Ponti, Jonas Pfeiffer, Sebastian Ruder, Goran Glavaš, Ivan Vulić, and Anna Korhonen. 2021. MAD-G: Multilingual adapter generation for efficient cross-lingual transfer. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4762–4781, Punta Cana,…
  6. Simone Balloccu, Patrı́cia Schmidtová, Mateusz Lango, and Ondřej Dušek. 2024. Leak, cheat, repeat: Data contamination and evaluation malpractices in closed-source llms. ArXiv preprint, abs/2402.03927. https://doi.org/10 .18653/v1/2024.eacl-long.5
  7. Lucas Bandarkar, Davis Liang, Benjamin Muller, Mikel Artetxe, Satya Narayan Shukla, Donald Husa, Naman Goyal, Abhinandan Krishnan, Luke Zettlemoyer, and Madian Khabsa. 2023. The Belebele Benchmark: A parallel reading comprehension dataset in 122 language variants. ArXiv preprint, abs/2308.16884. htt…
  8. Emily M. Bender. 2011. On achieving and evaluating language-independence in nlp. Linguistic Issues in Language Technology, 6. https://doi.org/10.33011/lilt.v6i.1239 — [doi:10.33011/lilt.v6i.1239](https://doi.org/10.33011/lilt.v6i.1239) ↗
  9. Terra Blevins and Luke Zettlemoyer. 2022. Language contamination helps explain the cross-lingual capabilities of english pretrained models. ArXiv preprint, abs/2204.08110. https://doi.org/10.18653/v1/2022 .emnlp-main.233 ↗
  10. Eleftheria Briakou, Colin Cherry, and George Foster. 2023. Searching for needles in a haystack: On the role of incidental bilingualism in PaLM’s translation capability. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9432–9452… find
  11. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter,… — Language Models are Few-Shot Learners
  12. Inigo Casanueva, Ivan Vulić, Georgios Spithourakis, and Paweł Budzianowski. 2022. NLU++: A multi-label, slot-rich, generalisable dataset for natural language understanding in task-oriented dialogue. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1998–2013, Seattle,… find
  13. Jiuhai Chen, Lichang Chen, Chen Zhu, and Tianyi Zhou. 2023. How many demonstrations do you need for in-context learning? In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 11149–11159. Singapore. Association for Computational Linguistics. https://doi.org/10.18653/v1 /202… find
  14. Yanda Chen, Ruiqi Zhong, Sheng Zha, George Karypis, and He He. 2022. Meta-learning via language model in-context tuning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 719–730, Dublin, Ireland. Association for Computational L… find
  15. Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan… find
  16. Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020. Unsupervised cross-lingual representation learning at scale. In Proceedings of the 58th Annual Meeting of the Association f… find
  17. Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel Bowman, Holger Schwenk, and Veselin Stoyanov. 2018. XNLI: Evaluating cross-lingual sentence representations. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2475–2485, Brussels, Bel… find
  18. Yiming Cui, Ziqing Yang, and Xin Yao. 2023. Efficient and effective text encoding for chinese llama and alpaca. ArXiv preprint, abs/2304.08177.
  19. John Dang, Shivalika Singh, Daniel D’souza, Arash Ahmadian, Alejandro Salamanca, Madeline Smith, Aidan Peppin, Sungjin Hong, Manoj Govindassamy, Terrence Zhao, Sandra Kublik, Meor Amer, Viraat Aryabumi, Jon Ander Campos, Yi-Chern Tan, Tom Kocmi, Florian Strub, Nathan Grinsztajn, Yannis Flet-Berliac,… — Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier
  20. Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023. Qlora: Efficient finetuning of quantized llms. ArXiv preprint, abs/2305.14314.
  21. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,…
  22. Sumanth Doddapaneni, Gowtham Ramesh, Mitesh M. Khapra, Anoop Kunchukuttan, and Pratyush Kumar. 2021. A primer on pretrained multilingual language models. ArXiv preprint, abs/2107.00676. — [arXiv:2107.00676](https://arxiv.org/abs/2107.00676) find
  23. Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, Arun Rao, Aston Zhang, Aurelien Rodriguez,… — The Llama 3 Herd of Models
  24. Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Naveen Arivazhagan, and Wei Wang. 2022. Language-agnostic BERT sentence embedding. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 878–891, Dublin, Ireland. Association for Computation…
  25. Gabor Fuisz, Ivan Vulic, Samuel Gibbons, Iñigo Casanueva, and Paweł Budzianowski. 2022. Improved and efficient conversational slot labeling through question answering. ArXiv preprint, abs/2204.02123. — [arXiv:2204.02123](https://arxiv.org/abs/2204.02123) find
  26. Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W. Mahoney, and Kurt Keutzer. 2022. A survey of quantization methods for efficient neural network inference. In Low-Power Computer Vision, pages 291–326. Chapman and Hall/CRC. https://doi.org/10 .1201/9781003162810-13 find
  27. Xiaochuang Han, Daniel Simig, Todor Mihaylov, Yulia Tsvetkov, Asli Celikyilmaz, and Tianlu Wang. 2023. Understanding in-context learning via supportive pretraining data. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12660–12… find
  28. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022. Lora: Low-rank adaptation of large language models. In the Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25–29, 2022. OpenReview.net.
  29. Songbo Hu, Han Zhou, Mete Hergul, Milan Gritta, Guchun Zhang, Ignacio Iacobacci, Ivan Vulić, and Anna Korhonen. 2023a. Multi 3 woz: A multilingual, multi-domain, multi-parallel dataset for training and evaluating culturally adapted task-oriented dialog systems. Transactions of the Association for C…
  30. Songbo Hu, Han Zhou, Moy Yuan, Milan Gritta, Guchun Zhang, Ignacio Iacobacci, Anna Korhonen, and Ivan Vulić. 2023b. A systematic study of performance disparities in multilingual task-oriented dialogue systems. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing… find
  31. Chip Huyen. 2022. Designing Machine Learning Systems. O’Reilly Media, Inc. find
  32. Ayyoob ImaniGooghari, Peiqin Lin, Amir Hossein Kargaran, Silvia Severini, Masoud Jalili Sabet, Nora Kassner, Chunlan Ma, Helmut Schmid, André Martins, François Yvon, and Hinrich Schütze. 2023. Glot500: Scaling multilingual corpora and language models to 500 languages. In Proceedings of the 61st A… find
  33. Amir Kargaran, Ayyoob Imani, François Yvon, and Hinrich Schuetze. 2023. GlotLID: Language identification for low-resource languages. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6155–6218, Singapore. Association for Computational Linguistics. https://doi.org/10.18…
  34. Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, and Xian Li. 2022. Few-shot learning with multilingual generative language models. In Proceedings of the 2022 Conference on E…
  35. Tannon Kew, Florian Schottmann, and Rico Sennrich. 2023. Turning english-centric llms into polyglots: How much multilinguality is needed? ArXiv preprint, abs/2312.12683. https://doi.org/10.18653/v1/2024 .findings-emnlp.766 ↗
  36. Anne Lauscher, Vinit Ravishankar, Ivan Vulić, and Goran Glavaš. 2020. From zero to hero: On the limitations of zero-shot language transfer with multilingual Transformers. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4483–4499, Online. Ass…
  37. Chengzu Li, Han Zhou, Goran Glavaš, Anna Korhonen, and Ivan Vuli. 2023a. On task performance and model calibration with supervised and self-ensembled in-context learning. find
  38. Haonan Li, Fajri Koto, Minghao Wu, Alham Fikri Aji, and Timothy Baldwin. 2023b. Bactrian-x: A multilingual replicable instruction-following model with low-rank adaptation. ArXiv preprint, abs/2305.15011.
  39. Tailin Liang, John Glossner, Lei Wang, Shaobo Shi, and Xiaotong Zhang. 2021. Pruning and quantization for deep neural network acceleration: A survey. Neurocomputing, 461:370–403. https://doi.org/10.1016/j.neucom.2021.07.045 ↗
  40. Chin-Yew Lin. 2004. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out, pages 74–81, Barcelona, Spain. Association for Computational Linguistics.
  41. Peiqin Lin, Shaoxiong Ji, Jörg Tiedemann, André F. T. Martins, and Hinrich Schütze. 2024. Mala-500: Massive language adaptation of large language models. ArXiv preprint, abs/2401.13303.
  42. Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin A. Raffel. 2022. Few-shot parameter-efficient fine-tuning is better and cheaper t…
  43. Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2023. Lost in the middle: How language models use long contexts. ArXiv preprint, abs/2307.03172.
  44. Ilya Loshchilov and Frank Hutter. 2019. Decoupled weight decay regularization. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6–9, 2019. OpenReview.net. find
  45. Sewon Min, Mike Lewis, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2022. MetaICL: Learning to learn in context. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2791–2809, Seattle, United States. A… find
  46. Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi. 2022. Cross-task generalization via natural language crowdsourcing instructions. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3470–3487, Dublin, Irelan… find
  47. Nikita Moghe, Evgeniia Razumovskaia, Liane Guillou, Ivan Vulić, Anna Korhonen, and Alexandra Birch. 2023. Multi3NLU++: A multilingual, multi-intent, multi-domain dataset for natural language understanding in task-oriented dialogue. In Findings of the Association for Computational Linguistics: ACL 2… find
  48. 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, and Colin Raffel… — Crosslingual Generalization through Multitask Finetuning
  49. Jessica Ojo, Kelechi Ogueji, Pontus Stenetorp, and David I. Adelani. 2023. How good are large language models on african languages? ArXiv preprint, abs/2311.07978. — [arXiv:2311.07978](https://arxiv.org/abs/2311.07978) find
  50. Lucas Page-Caccia, Edoardo Maria Ponti, Zhan Su, Matheus Pereira, Nicolas Le Roux, and Alessandro Sordoni. 2024. Multi-head adapter routing for cross-task generalization. Advances in Neural Information Processing Systems, 36. find
  51. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: A method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 311–318, Philadelphia, Pennsylvania, USA. Association for Computational…
  52. Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, and Zaı̈d Harchaoui. 2021. MAUVE: measuring the gap between neural text and human text using divergence frontiers. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Infor…
  53. Edoardo Maria Ponti, Goran Glavaš, Olga Majewska, Qianchu Liu, Ivan Vulić, and Anna Korhonen. 2020. XCOPA: A multilingual dataset for causal commonsense reasoning. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2362–2376, Online. Associatio…
  54. Ofir Press, Noah A. Smith, and Mike Lewis. 2022. Train short, test long: Attention with linear biases enables input length extrapolation. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25–29, 2022. OpenReview.net. find
  55. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9. find
  56. Evgeniia Razumovskaia, Goran Glavaš, Anna Korhonen, and Ivan Vulić. 2023. Sqatin: Supervised instruction tuning meets question answering for improved dialogue nlu. ArXiv preprint, abs/2311.09502. find
  57. Melissa Roemmele, Cosmin Adrian Bejan, and Andrew S. Gordon. 2011. Choice of plausible alternatives: An evaluation of commonsense causal reasoning. In 2011 AAAI Spring Symposium Series. find
  58. Ohad Rubin and Jonathan Berant. 2023. Long-range language modeling with self-retrieval. ArXiv preprint, abs/2306.13421. — [arXiv:2306.13421](https://arxiv.org/abs/2306.13421) find
  59. Sebastian Ruder, Jonathan Clark, Alexander Gutkin, Mihir Kale, Min Ma, Massimo Nicosia, Shruti Rijhwani, Parker Riley, Jean-Michel Sarr, Xinyi Wang, John Wieting, Nitish Gupta, Anna Katanova, Christo Kirov, Dana Dickinson, Brian Roark, Bidisha Samanta, Connie Tao, David Adelani, Vera Axelrod, Isaac… find
  60. Sebastian Ruder, Noah Constant, Jan Botha, Aditya Siddhant, Orhan Firat, Jinlan Fu, Pengfei Liu, Junjie Hu, Dan Garrette, Graham Neubig, and Melvin Johnson. 2021. XTREME-R: Towards more challenging and nuanced multilingual evaluation. In Proceedings of the 2021 Conference on Empirical Methods in Nat…
  61. Oscar Sainz, Jon Campos, Iker Garcı́a-Ferrero, Julen Etxaniz, Oier Lopez de Lacalle, and Eneko Agirre. 2023. Nlp evaluation in trouble: On the need to measure llm data contamination for each benchmark. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 10776–10787. https…
  62. Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M. Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonath… — Multitask Prompted Training Enables Zero-Shot Task Generalization
  63. Uri Shaham, Jonathan Herzig, Roee Aharoni, Idan Szpektor, Reut Tsarfaty, and Matan Eyal. 2024. Multilingual instruction tuning with just a pinch of multilinguality. ArXiv preprint, abs/2401.01854. https://doi.org/10 .18653/v1/2024.findings-acl.136
  64. Freda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang, Suraj Srivats, Soroush Vosoughi, Hyung Won Chung, Yi Tay, Sebastian Ruder, Denny Zhou, Dipanjan Das, and Jason Wei. 2022. Language models are multilingual chain-of-thought reasoners. In the Eleventh International Conference on Learning Representa…
  65. Oleh Shliazhko, Alena Fenogenova, Maria Tikhonova, Vladislav Mikhailov, Anastasia Kozlova, and Tatiana Shavrina. 2022. mgpt: Few-shot learners go multilingual. ArXiv preprint, abs/2204.07580.
  66. Sunayana Sitaram, Monojit Choudhury, Barun Patra, Vishrav Chaudhary, Kabir Ahuja, and Kalika Bali. 2023. Everything you need to know about multilingual llms: Towards fair, performant and reliable models for languages of the world. In Proceedings of the 61st Annual Meeting of the Association for Comp… find
  67. Eshaan Tanwar, Subhabrata Dutta, Manish Borthakur, and Tanmoy Chakraborty. 2023. Multilingual LLMs are better cross-lingual in-context learners with alignment. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6292–6307, Toronto…
  68. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller,… — Llama 2: Open Foundation and Fine-Tuned Chat Models
  69. Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le. 2022a. Finetuned language models are zero-shot learners. In the Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25–29, 2022. OpenRevie…
  70. Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. 2022b. Emergent abilities of large language models. Transactions on M…
  71. Xiangpeng Wei, Haoran Wei, Huan Lin, Tianhao Li, Pei Zhang, Xingzhang Ren, Mei Li, Yu Wan, Zhiwei Cao, Binbin Xie, Tianxiang Hu, Shangjie Li, Binyuan Hui, Bowen Yu, Dayiheng Liu, Baosong Yang, Fei Huang, and Jun Xie. 2023. Polylm: An open source polyglot large language model. ArXiv preprint, abs/230… find
  72. Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. A broad-coverage challenge corpus for sentence understanding through inference. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Pa…
  73. Genta Indra Winata, Andrea Madotto, Zhaojiang Lin, Rosanne Liu, Jason Yosinski, and Pascale Fung. 2021. Language models are few-shot multilingual learners. In Proceedings of the 1st Workshop on Multilingual Representation Learning, pages 1–15, Punta Cana, Dominican Republic. Association for Computat…
  74. Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021. mT5: A massively multilingual pre-trained text-to-text transformer. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Ling…
  75. Junjie Ye, Xuanting Chen, Nuo Xu, Can Zu, Zekai Shao, Shichun Liu, Yuhan Cui, Zeyang Zhou, Chao Gong, Yang Shen, Jie Zhou, Siming Chen, Tao Gui, Qi Zhang, and Xuanjing Huang. 2023. A comprehensive capability analysis of gpt-3 and gpt-3.5 series models. ArXiv preprint, abs/2303.10420. find
  76. Jun Zhao, Zhihao Zhang, Qi Zhang, Tao Gui, and Xuanjing Huang. 2024. Llama beyond english: An empirical study on language capability transfer. ArXiv preprint, abs/2401.01055. find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/razumovskaia-2025-analyzing-adapting-large-language-models/