---
title: "QAmeleon: Multilingual QA with Only 5 Examples (2023)"
description: "QAmeleon tests whether a handful of native examples can replace large-scale annotation. 50 references, cited by 29 papers here."
authors: ["Priyanka Agrawal", "Chris Alberti", "Fantine Huot", "Joshua Maynez", "Ji Ma", "Sebastian Ruder", "Kuzman Ganchev", "Dipanjan Das", "Mirella Lapata"]
year: 2023
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/2023.tacl-1.98/"
doi: "10.1162/tacl_a_00625"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/agrawal-2023-qameleon-multilingual-qa-only-5/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/agrawal-2023-qameleon-multilingual-qa-only-5/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2023](https://md.research.elamur.ai/free/en/papers/2023/page/11/) / QAmeleon: Multilingual QA with Only 5 Examples

# QAmeleon: Multilingual QA with Only 5 Examples

**Authors:** [Priyanka Agrawal](https://md.research.elamur.ai/free/en/authors/priyanka-agrawal/), [Chris Alberti](https://md.research.elamur.ai/free/en/authors/chris-alberti/), [Fantine Huot](https://md.research.elamur.ai/free/en/authors/fantine-huot/), [Joshua Maynez](https://md.research.elamur.ai/free/en/authors/joshua-maynez/), [Ji Ma](https://md.research.elamur.ai/free/en/authors/ji-ma/), [Sebastian Ruder](https://md.research.elamur.ai/free/en/authors/sebastian-ruder/), [Kuzman Ganchev](https://md.research.elamur.ai/free/en/authors/kuzman-ganchev/), [Dipanjan Das](https://md.research.elamur.ai/free/en/authors/dipanjan-das/) and [Mirella Lapata](https://md.research.elamur.ai/free/en/authors/mirella-lapata/) · **Year:** 2023 · **Venue:** [Transactions of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/tacl/) ([Transactions of the Association for Computational Linguistics (TACL) 2023](https://md.research.elamur.ai/free/en/venues/tacl/2023/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2023.tacl-1.98/) · [PDF](https://aclanthology.org/2023.tacl-1.98.pdf) · [DOI 10.1162/tacl\_a\_00625](https://doi.org/10.1162/tacl_a_00625) · [OpenAlex](https://openalex.org/W4390075256)

**Citations:** 16 citations (OpenAlex, 2026-10-14) · [29](https://md.research.elamur.ai/free/en/papers/agrawal-2023-qameleon-multilingual-qa-only-5/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Extractive question answering datasets are large mainly for English, so QA systems in other languages usually depend on zero-shot transfer or machine-translated training data. Both options bring translationese and English-centric biases. QAmeleon tests whether a handful of native examples can replace large-scale annotation. For each target language, the authors take only five gold (passage, question, answer) triples and use them to prompt-tune a large pre-trained language model. The model's weights stay frozen, and only a short soft prompt is learned. The tuned model then produces answers and questions for unlabeled in-language Wikipedia passages, with English acting as an intermediate step. The resulting synthetic corpus is used to fine-tune a separate QA model. On TyDiQA-GoldP and MLQA, this approach beats English-only training, translate-train, and few-shot prompt engineering. It also recovers most of the gap to a fully supervised system. The results suggest that cheap few-shot data synthesis can stand in for expensive multilingual annotation in under-resourced languages.

## Key points
- **Setup:** 5 labeled examples per language are used for prompt tuning (Lester et al., 2021). The soft prompt is 50 tokens long, and the PLM parameters are left unchanged. Prompts generate an English answer or question before the target-language one, a multilingual chain-of-thought-style bridge.
- **Results on TyDiQA-GoldP:**
  - About +12 points absolute accuracy over an English-only baseline.
  - About +4 points over a strong translate-train baseline.
  - Closes roughly 60% of the distance to a supervised upper bound trained on about 50,000 hand-labeled examples.
- **Evaluation and scaling findings:** Quality is judged by downstream QA performance rather than BLEU or ROUGE, which the authors show can be misleading. Increasing the manual examples from 50 to 100 brought little extra benefit.
- **Released resource:** 47,173 synthetic 5-shot examples (passage, question, extractive answer), about 89% of the data used. Each item was screened by three human raters for sensitive content. The data is available on GitHub at google-research-datasets/QAmeleon.
- **Limitations:** The method is expensive, at roughly 256 TPU v3 chips for 12 hours per experiment. The results are also tied to one specific large PLM.

*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

- [Google DeepMind](https://md.research.elamur.ai/free/en/companies/google-deepmind/) — Company, London, United Kingdom · 72 works · 84,326 citations
- [Google (United Kingdom)](https://md.research.elamur.ai/free/en/companies/google-united-kingdom/) — Company, London, United Kingdom · 11 works · 2,450 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works

🧭 Topics (2): [🌍 Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · 882 works · [❓ Question answering](https://md.research.elamur.ai/free/en/topics/question-answering/) · 487 works

## Cite

Cite as: Priyanka Agrawal, Chris Alberti, Fantine Huot, Joshua Maynez, Ji Ma, Sebastian Ruder, Kuzman Ganchev, Dipanjan Das and Mirella Lapata. 2023. QAmeleon: Multilingual QA with Only 5 Examples. Transactions of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{agrawal2023qameleon,
  title = {{QAmeleon: Multilingual QA with Only 5 Examples}},
  author = {Priyanka Agrawal, Chris Alberti, Fantine Huot, Joshua Maynez, Ji Ma, Sebastian Ruder, Kuzman Ganchev, Dipanjan Das and Mirella Lapata},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2023},
  doi = {10.1162/tacl\_a\_00625},
  url = {https://doi.org/10.1162/tacl_a_00625}
}
```

- [← previous Lost in Translation: Large Language Models in Non-English Content Analysis](https://md.research.elamur.ai/free/en/papers/nicholas-2023-lost-translation-large-language-models/)
- [next → Scaling Laws for Generative Mixed-Modal Language Models](https://md.research.elamur.ai/free/en/papers/aghajanyan-2023-scaling-laws-generative-mixed-modal/)
- [↑ back to papers of 2023](https://md.research.elamur.ai/free/en/papers/2023/page/11/)

## References

50 references, 33 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Chris Alberti, Daniel Andor, Emily Pitler, Jacob Devlin, and Michael Collins. 2019. [Synthetic QA corpora generation with roundtrip consistency](https://md.research.elamur.ai/free/en/papers/alberti-2019-synthetic-qa-corpora-generation-roundtrip/). In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6168–6173, Florence, Italy. Association for Computational…
2. Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2020a. [Translation artifacts in cross-lingual transfer learning](https://md.research.elamur.ai/free/en/papers/artetxe-2020-translation-artifacts-cross-lingual-transfer/). In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7674–7684, Online. Association for Computational Linguistics. [https://doi.org](https://doi.org) /10.1… [↗](https://doi.org)
3. Mikel Artetxe, Sebastian Ruder, and Dani Yogatama. 2020b. [On the cross-lingual transferability of monolingual representations](https://md.research.elamur.ai/free/en/papers/artetxe-2020-cross-lingual-transferability-monolingual-representations/). In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4623–4637, Online. Association for Computational Linguistics. [https://doi.o…](https://doi.o…) [↗](https://doi.o…)
4. Giusepppe Attardi. 2015. [Wikiextractor](https://md.research.elamur.ai/free/en/papers/bajaj-2016-ms-marco-human-generated/). [https://github.com/attardi/wikiextractor](https://github.com/attardi/wikiextractor) Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, and Tong Wang. 2016. M… [↗](https://github.com/attardi/wikiextractor)
5. Max Bartolo, Tristan Thrush, Robin Jia, Sebastian Riedel, Pontus Stenetorp, and Douwe Kiela. 2021. Improving question answering model robustness with synthetic adversarial data generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8830–8848, Onl… [find](https://scholar.google.com/scholar?q=Max+Bartolo%2C+Tristan+Thrush%2C+Robin+Jia%2C+Sebastian+Riedel%2C+Pontus+Stenetorp%2C+and+Douwe+Kiela%2E+2021%2E+Improving+question+answering+model+robustness+with+synthetic+adversarial+data+generation%2E+In+Proceedi)
6. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. 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](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
7. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhai… — [Evaluating Large Language Models Trained on Code](https://md.research.elamur.ai/free/en/papers/chen-2021-evaluating-large-language-models-trained/)
8. 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 Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Sl… [find](https://scholar.google.com/scholar?q=Xuezhi+Wang%2C+Mostafa+Dehghani%2C+Siddhartha+Brahma%2C+Albert+Webson%2C+Shixiang+Shane+Gu%2C+Zhuyun+Dai%2C+Mirac+Suzgun%2C+Xinyun+Chen%2C+Aakanksha+Chowdhery%2C+Alex+Castro%2DRos%2C+Marie+Pellat%2C+Kevin+Robinson%2C+Dasha+Val)
9. Zewen Chi, Li Dong, Furu Wei, Wenhui Wang, Xian-Ling Mao, and Heyan Huang. 2020. Cross-lingual natural language generation via pre-training. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 7570–7577. https://doi.org/10 .1609/aaai.v34i05.6256 [find](https://scholar.google.com/scholar?q=Zewen+Chi%2C+Li+Dong%2C+Furu+Wei%2C+Wenhui+Wang%2C+Xian%2DLing+Mao%2C+and+Heyan+Huang%2E+2020%2E+Cross%2Dlingual+natural+language+generation+via+pre%2Dtraining%2E+In+Proceedings+of+the+AAAI+Conference+on+Artificial+Intelli)
10. Zewen Chi, Shaohan Huang, Li Dong, Shuming Ma, Bo Zheng, Saksham Singhal, Payal Bajaj, Xia Song, Xian-Ling Mao, He-Yan Huang, and Furu Wei. 2022. [Xlm-e: Cross-lingual language model pre-training via electra](https://md.research.elamur.ai/free/en/papers/chi-2022-xlm-e-cross-lingual-language/). In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (…
11. Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emi… — [PaLM: Scaling Language Modeling with Pathways](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/)
12. Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Jonathan H. Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki. 2020. [TyDi QA: A benchmark for information-seeking question answering in typologically diver…](https://md.research.elamur.ai/free/en/papers/clark-2020-tydi-qa-benchmark-information-seeking/)
13. Zhuyun Dai, Vincent Y Zhao, Ji Ma, Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin Guu, Keith B. Hall, and Ming-Wei Chang. 2022. [Promptagator: Few-shot dense retrieval from 8 examples](https://md.research.elamur.ai/free/en/papers/dai-2022-promptagator-few-shot-dense-retrieval/). arXiv preprint arXiv:2209 .11755.
14. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. [BERT: Pre-training of deep bidirectional transformers for language understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/). In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,…
15. Dan Garrette and Jason Baldridge. 2013. [Learning a part-of-speech tagger from two hours of annotation](https://md.research.elamur.ai/free/en/papers/garrette-2013-learning-part-speech-tagger-two/). In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 138–147, Atlanta, Georgia. Association for C…
16. Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, and Hannaneh Hajishirzi. 2020. UNI-FIEDQA: Crossing format boundaries with a single QA system. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1896–1907, Online. Association for Co… — [doi:10.18653/v1/2020.findings-emnlp](https://doi.org/10.18653/v1/2020.findings-emnlp) [find](https://scholar.google.com/scholar?q=Daniel+Khashabi%2C+Sewon+Min%2C+Tushar+Khot%2C+Ashish+Sabharwal%2C+Oyvind+Tafjord%2C+Peter+Clark%2C+and+Hannaneh+Hajishirzi%2E+2020%2E+UNI%2DFIEDQA%3A+Crossing+format+boundaries+with+a+single+QA+system%2E+In+Findings+of+th)
17. Moshe Koppel and Noam Ordan. 2011. [Translationese and its dialects](https://md.research.elamur.ai/free/en/papers/koppel-2011-translationese-its-dialects/). In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 1318–1326, Portland, Oregon, USA. Association for Computational Linguistics.
18. Alina Kramchaninova and Arne Defauw. 2022. Synthetic data generation for multilingual domain-adaptable question answering systems. In Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, pages 151–160, Ghent, Belgium. European Association for Machine Transla… [find](https://scholar.google.com/scholar?q=Alina+Kramchaninova+and+Arne+Defauw%2E+2022%2E+Synthetic+data+generation+for+multilingual+domain%2Dadaptable+question+answering+systems%2E+In+Proceedings+of+the+23rd+Annual+Conference+of+the+European+Associat)
19. Vishwajeet Kumar, Nitish Joshi, Arijit Mukherjee, Ganesh Ramakrishnan, and Preethi Jyothi. 2019. Cross-lingual training for automatic question generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4863–4872, Florence, Italy. Association for Com… [find](https://scholar.google.com/scholar?q=Vishwajeet+Kumar%2C+Nitish+Joshi%2C+Arijit+Mukherjee%2C+Ganesh+Ramakrishnan%2C+and+Preethi+Jyothi%2E+2019%2E+Cross%2Dlingual+training+for+automatic+question+generation%2E+In+Proceedings+of+the+57th+Annual+Meeting+of+)
20. Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019. [Natural…](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2019-natural-questions-benchmark-question-answering/)
21. Kyungjae Lee, Kyoungho Yoon, Sunghyun Park, and Seung-won Hwang. 2018. Semi-supervised training data generation for multilingual question answering. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources… [find](https://scholar.google.com/scholar?q=Kyungjae+Lee%2C+Kyoungho+Yoon%2C+Sunghyun+Park%2C+and+Seung%2Dwon+Hwang%2E+2018%2E+Semi%2Dsupervised+training+data+generation+for+multilingual+question+answering%2E+In+Proceedings+of+the+Eleventh+International+Confer)
22. Brian Lester, Rami Al-Rfou, and Noah Constant. 2021. [The power of scale for parameter-efficient prompt tuning](https://md.research.elamur.ai/free/en/papers/lester-2021-power-scale-parameter-efficient-prompt/). In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3045–3059, Online and Punta Cana, Dominican Republic. Association for Computational Linguist…
23. Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020a. [BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/). In Proceedings of the 58th Annual Meeting of th…
24. Patrick Lewis, Barlas Oguz, Ruty Rinott, Sebastian Riedel, and Holger Schwenk. 2020b. [MLQA: Evaluating cross-lingual extractive question answering](https://md.research.elamur.ai/free/en/papers/lewis-2019-mlqa-evaluating-cross-lingual-extractive/). In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7315–7330, Online. Association for Computational Lingu…
25. Patrick Lewis, Yuxiang Wu, Linqing Liu, Pasquale Minervini, Heinrich Küttler, Aleksandra Piktus, Pontus Stenetorp, and Sebastian Riedel. 2021. [PAQ: 65 million probably-asked questions and what you can do with them](https://md.research.elamur.ai/free/en/papers/lewis-2021-paq-65-million-probably-asked/). Transactions of the Association for Computational Linguistics, 9:1098–1115. https://…
26. Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyano… — [Few-shot Learning with Multilingual Language Models](https://md.research.elamur.ai/free/en/papers/lin-2021-few-shot-learning-multilingual-language/)
27. Yu-Hsiang Lin, Chian-Yu Chen, Jean Lee, Zirui Li, Yuyan Zhang, Mengzhou Xia, Shruti Rijhwani, Junxian He, Zhisong Zhang, Xuezhe Ma, Antonios Anastasopoulos, Patrick Littell, and Graham Neubig. 2019. [Choosing transfer languages for cross-lingual learning](https://md.research.elamur.ai/free/en/papers/lin-2019-choosing-transfer-languages-cross-lingual/). In Proceedings of ACL 2019.
28. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. [BLEU: A method for automatic evaluation of machine translation](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/). In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 311–318. https://doi.org/10.3115/1073083.1073135 [↗](https://doi.org/10.3115/1073083.1073135)
29. Colin Raffel, Noam Shazeer, Adam Roberts, Katherin Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. [Exploring the limits of transfer learning with a unified text-to-text transformer](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/). Journal of Machine Learning Research, 21(140):1–67.
30. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. [SQuAD: 100,000+ questions for machine comprehension of text](https://md.research.elamur.ai/free/en/papers/rajpurkar-2016-squad-100-000-questions-machine/). In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2383–2392, Austin, Texas. Association for Computational Linguistics.…
31. Arij Riabi, Thomas Scialom, Rachel Keraron, Benoı̂t Sagot, Djamé Seddah, and Jacopo Staiano. 2021. [Synthetic data augmentation for zero-shot cross-lingual question answering](https://md.research.elamur.ai/free/en/papers/schick-2021-it-s-not-just-size/). In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7016–7030, Online and Punta…
32. Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020. [Bleurt: Learning robust metrics for text generation](https://md.research.elamur.ai/free/en/papers/sellam-2020-bleurt-learning-robust-metrics-text/). In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7881–7892. https://doi.org/10.18653/v1/2020 .acl-main.704 [↗](https://doi.org/10.18653/v1/2020)
33. Siamak Shakeri, Noah Constant, Mihir Kale, and Linting Xue. 2021. Towards zero-shot multilingual synthetic question and answer generation for cross-lingual reading comprehension. In Proceedings of the 14th International Conference on Natural Language Generation, pages 35–45, Aberdeen, Scotland, UK.… [find](https://scholar.google.com/scholar?q=Siamak+Shakeri%2C+Noah+Constant%2C+Mihir+Kale%2C+and+Linting+Xue%2E+2021%2E+Towards+zero%2Dshot+multilingual+synthetic+question+and+answer+generation+for+cross%2Dlingual+reading+comprehension%2E+In+Proceedings+of+the)
34. Siamak Shakeri, Cicero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Feng Nan, Zhiguo Wang, Ramesh Nallapati, and Bing Xiang. 2020. End-to-end synthetic data generation for domain adaptation of question answering systems. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language… [find](https://scholar.google.com/scholar?q=Siamak+Shakeri%2C+Cicero+Nogueira+dos+Santos%2C+Henghui+Zhu%2C+Patrick+Ng%2C+Feng+Nan%2C+Zhiguo+Wang%2C+Ramesh+Nallapati%2C+and+Bing+Xiang%2E+2020%2E+End%2Dto%2Dend+synthetic+data+generation+for+domain+adaptation+of+questi)
35. Noam Shazeer and Mitchell Stern. 2018. Adafactor: Adaptive learning rates with sublinear memory cost. In International Conference on Machine Learning, pages 4596–4604. PMLR. [find](https://scholar.google.com/scholar?q=Noam+Shazeer+and+Mitchell+Stern%2E+2018%2E+Adafactor%3A+Adaptive+learning+rates+with+sublinear+memory+cost%2E+In+International+Conference+on+Machine+Learning%2C+pages+4596%E2%80%934604%2E+PMLR%2E)
36. 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](https://md.research.elamur.ai/free/en/papers/ruder-2021-xtreme-r-more-challenging-nuanced/). In Proceedings of the 2021 Conference on Empirical Methods in Nat…
37. 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](https://md.research.elamur.ai/free/en/papers/shi-2022-language-models-multilingual-chain-thought/). https://doi.org/10 .48550/ARXIV.2210.03057
38. Timo Schick and Hinrich Schütze. 2021. It’s not just size that matters: Small language models Tianyi Tang, Junyi Li, Wayne Xin Zhao, and Ji-Rong Wen. 2022. [find](https://scholar.google.com/scholar?q=Timo+Schick+and+Hinrich+Schu%CC%88tze%2E+2021%2E+It%E2%80%99s+not+just+size+that+matters%3A+Small+language+models+Tianyi+Tang%2C+Junyi+Li%2C+Wayne+Xin+Zhao%2C+and+Ji%2DRong+Wen%2E+2022%2E)
39. Context-tuning: Learning Tom Sherborne and Mirella Lapata. 2022. Meta-learning a cross-lingual manifold for semantic parsing. https://doi.org/10.1162/tacl a 00533 contextualized prompts for natural language generation. https://doi.org/10.48550/ARXIV.2201.08670 for Computational Linguistics, 10:291–… [↗](https://doi.org/10.1162/tacl)
40. Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2017. [News-QA: A machine comprehension dataset](https://md.research.elamur.ai/free/en/papers/trischler-2017-newsqa-machine-comprehension-dataset/). In Proceedings of the 2nd Workshop on Representation Learning for NLP, pages 191–200, Vancouver, Canada. Association for Computational Lingui…
41. 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](https://md.research.elamur.ai/free/en/papers/xue-2021-mt5-massively-multilingual-pre-trained/). In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Ling…
42. Tu Vu, Aditya Barua, Brian Lester, Daniel Cer, Mohit Iyyer, and Noah Constant. 2022. Overcoming catastrophic forgetting in zero-shot cross-lingual generation. https://doi.org/10.18653/v1/2022.emnlp-main.630 [↗](https://doi.org/10.18653/v1/2022.emnlp-main.630)
43. Kevin Yang, Yuandong Tian, Nanyun Peng, and Dan Klein. 2022. Re3: Generating longer stories with recursive reprompting and revision. https://doi.org/10.18653/v1/2022 .emnlp-main.296 [↗](https://doi.org/10.18653/v1/2022)
44. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou. 2022. [Chain of thought prompting elicits reasoning in large language models](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/). arXiv preprint arXiv:2201.11903.
45. Genta Indra Winata, Andrea Madotto, Zhaojiang Lin, Rosanne Liu, Jason Yosinski, and Pascale Fung. 2021. [Language models are few-shot multilingual learners](https://md.research.elamur.ai/free/en/papers/winata-2021-language-models-are-few-shot/). In Proceedings of the 1st Workshop on Multilingual Representation Learning, pages 1–15, Punta Cana, Dominican Republic. Association for Computat…
46. Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, N… [find](https://scholar.google.com/scholar?q=Yonghui+Wu%2C+Mike+Schuster%2C+Zhifeng+Chen%2C+Quoc+V%2E+Le%2C+Mohammad+Norouzi%2C+Wolfgang+Macherey%2C+Maxim+Krikun%2C+Yuan+Cao%2C+Qin+Gao%2C+Klaus+Macherey%2C+Jeff+Klingner%2C+Apurva+Shah%2C+Melvin+Johnson%2C+Xiaobing+Liu%2C+%C5%81uk)
47. Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, and Colin Raffel. 2022. [Byt5: Towards a token-free future with pre-trained byte-to-byte models](https://md.research.elamur.ai/free/en/papers/yang-2018-hotpotqa-dataset-diverse-explainable-multi/). Transactions of the Association Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan…
48. Bingsheng Yao, Dakuo Wang, Tongshuang Wu, Zheng Zhang, Toby Li, Mo Yu, and Ying Xu. 2022. [It is AI’s turn to ask humans a question: Question-answer pair generation for children’s story books](https://md.research.elamur.ai/free/en/papers/yao-2022-it-is-ai-s-turn/). In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long P…
49. Mengjie Zhao and Hinrich Schütze. 2021. Discrete and soft prompting for multilingual models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8547–8555, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. [https://doi.…](https://doi.…) [↗](https://doi.…)
50. Mengjie Zhao, Yi Zhu, Ehsan Shareghi, Ivan Vulić, Roi Reichart, Anna Korhonen, and Hinrich Schütze. 2021. A closer look at few-shot crosslingual transfer: The choice of shots matters. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th Internatio… [find](https://scholar.google.com/scholar?q=Mengjie+Zhao%2C+Yi+Zhu%2C+Ehsan+Shareghi%2C+Ivan+Vulic%CC%81%2C+Roi+Reichart%2C+Anna+Korhonen%2C+and+Hinrich+Schu%CC%88tze%2E+2021%2E+A+closer+look+at+few%2Dshot+crosslingual+transfer%3A+The+choice+of+shots+matters%2E+In+Proceedings)

*Updated 30 October 2026*