Papers by Phong Nguyen-Thuan Do
Revealing Weaknesses of Vietnamese Language Models Through Unanswerable Questions in Machine Reading Comprehension (2023.eacl-srw)
Copied to clipboard
| Challenge: | Existing problems in Vietnamese Machine Reading Comprehension systems are limited due to multilinguality, which limits the ability of multilingual models to develop state-of-the-art systems. |
| Approach: | They propose to modify the process of annotating unanswerable questions to improve the quality of unanswered questions to a higher level of difficulty for Machine Reading Comprehension systems to solve. |
| Outcome: | The proposed modification improves the quality of unanswerable questions to a higher level of difficulty for Machine Reading Comprehension systems to solve. |
VMLU Benchmarks: A comprehensive benchmark toolkit for Vietnamese LLMs (2025.acl-long)
Copied to clipboard
Cuc Thi Bui, Nguyen Truong Son, Truong Van Trang, Lam Viet Phung, Pham Nhut Huy, Hoang Anh Le, Quoc Huu Van, Phong Nguyen-Thuan Do, Van Le Tran Truc, Duc Thanh Chau, Le-Minh Nguyen
| Challenge: | The evolution of Large Language Models (LLMs) has underscored the need for benchmarks designed for various languages and cultural contexts. |
| Approach: | They propose to use Vietnamese multitask language understanding (VMLU) benchmarks to assess different capabilities of LLMs, including general knowledge, reading comprehension, reasoning, and conversational skills. |
| Outcome: | The VMLU Benchmarks assess LLMs' general knowledge, reading comprehension, reasoning, and conversational skills. |
The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models (2023.eacl-main)
Copied to clipboard
| Challenge: | Pretrained language models have achieved super-human performances on many Machine Reading Comprehension (MRC) benchmarks. |
| Approach: | They propose to fine-tune three state-of-the-art language models on SQuAD 1.1 or SQu AD 2.0 and then evaluate their robustness under adversarial attacks. |
| Outcome: | The proposed model is able to perform better under adversarial attacks than model fine-tuned on SQuAD 1.1 or 2.0. |