Papers by Wei-Yin Ko
Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning (2024.acl-long)
Copied to clipboard
Shivalika Singh, Freddie Vargus, Daniel D’souza, Börje Karlsson, Abinaya Mahendiran, Wei-Yin Ko, Herumb Shandilya, Jay Patel, Deividas Mataciunas, Laura O’Mahony, Mike Zhang, Ramith Hettiarachchi, Joseph Wilson, Marina Machado, Luisa Moura, Dominik Krzemiński, Hakimeh Fadaei, Irem Ergun, Ifeoma Okoh, Aisha Alaagib, Oshan Mudannayake, Zaid Alyafeai, Vu Chien, Sebastian Ruder, Surya Guthikonda, Emad Alghamdi, Sebastian Gehrmann, Niklas Muennighoff, Max Bartolo, Julia Kreutzer, Ahmet Üstün, Marzieh Fadaee, Sara Hooker
| Challenge: | Existing datasets in the English language are mostly in the realm of instruction fine-tuning . aya dataset, the Aya Collection, and the AYa Evaluation Suite are key resources . |
| Approach: | They aim to build a human-curated instruction-following dataset spanning 65 languages . they work with fluent speakers of languages from around the world to collect natural instances of instructions and completions . |
| Outcome: | The goal is to build a human-curated instruction-following dataset spanning 65 languages. |
Understanding and Mitigating Language Confusion in LLMs (2024.emnlp-main)
Copied to clipboard
| Challenge: | Llama Instruct and Mistral models exhibit high degrees of language confusion and even the strongest models fail to consistently respond in the correct language. |
| Approach: | They develop a language confusion benchmark to evaluate LLMs' inability to consistently generate text in a user’s desired language. |
| Outcome: | The proposed model fails to consistently respond in the correct language, despite being prone to high temperatures and complex prompts. |
Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation (2025.acl-long)
Copied to clipboard
Shivalika Singh, Angelika Romanou, Clémentine Fourrier, David Ifeoluwa Adelani, Jian Gang Ngui, Daniel Vila-Suero, Peerat Limkonchotiwat, Kelly Marchisio, Wei Qi Leong, Yosephine Susanto, Raymond Ng, Shayne Longpre, Sebastian Ruder, Wei-Yin Ko, Antoine Bosselut, Alice Oh, Andre Martins, Leshem Choshen, Daphne Ippolito, Enzo Ferrante, Marzieh Fadaee, Beyza Ermis, Sara Hooker
| Challenge: | Reliable multilingual evaluation is difficult and culturally appropriate evaluation is even harder to achieve. |
| Approach: | They propose a multilingual evaluation framework that aims to mitigate these biases by improving translations and annotation practices. |
| Outcome: | The proposed framework improves translation quality and cultural coverage and is culturally sensitive and culturally agnostic. |