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TyDi QA

Parameter-efficient fine-tuning (PEFT) using labeled task data can significantly improve the performance of large language models (LLMs) on the downstream task. However, there are 7000 languages in the world and many of these languages lack labeled data for real-world language generation tasks.

Data and Resources

Cite this as

Pinzhen Chen, Shaoxiong Ji, Nikolay Bogoychev, Andrey Kutuzov, Barry Haddow, Kenneth Heafield (2024). Dataset: TyDi QA. https://doi.org/10.57702/5nbxtd9h

DOI retrieved: December 2, 2024

Additional Info

Field Value
Created December 2, 2024
Last update December 2, 2024
Defined In https://doi.org/10.48550/arXiv.2211.15613
Citation
  • https://doi.org/10.48550/arXiv.2311.09344
Author Pinzhen Chen
More Authors
Shaoxiong Ji
Nikolay Bogoychev
Andrey Kutuzov
Barry Haddow
Kenneth Heafield
Homepage https://github.com/tydi-institute/tydiqa