Papers by Minh Ngoc Ta
FAID: Fine-grained AI-generated Text Detection using Multi-task Auxiliary and Multi-level Contrastive Learning (2026.eacl-long)
Copied to clipboard
Minh Ngoc Ta, Dong Cao Van, Duc-Anh Hoang, Minh Le-Anh, Truong Nguyen, My Anh Tran Nguyen, Yuxia Wang, Preslav Nakov, Dinh Viet Sang
| Challenge: | Existing binary detection frameworks for human-written, LLM-generated and human-LLM collaborative texts are challenging . a recent study focused on binary detection, i.e., human vs. LLM, or on fine-grained detection limited to English. |
| Approach: | They propose a fine-grained detection framework to classify text into three categories . they use multilingual datasets and a multi-domain, multi-generator dataset . |
| Outcome: | The proposed framework outperforms baselines on unseen domains and new LLMs. |
ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning (2026.acl-demo)
Copied to clipboard
Vladislav Smirnov, Quang-Chieu Nguyen, Sergey Senichev, Minh Ngoc Ta, Ekaterina Fadeeva, Artem Vazhentsev, Daria Galimzianova, Nikolai Rozanov, Viktor Mazanov, Jingwei Ni, Tianyi Wu, Igor Kiselev, Mrinmaya Sachan, Iryna Gurevych, Preslav Nakov, Timothy Baldwin, Artem Shelmanov
| Challenge: | Existing TTC scaling strategies and reasoning scorers are fragmented and evaluated under inconsistent protocols. |
| Approach: | They propose a framework for seamless test-time compute scaling of large language model reasoning . they use a modular Python library to implement state-of-the-art scaling strategy and scorer families . |
| Outcome: | The proposed framework evaluates performance and computational efficiency on mathematical and coding tasks. |
Is Human-Like Text Liked by Humans? Multilingual Human Detection and Preference Against AI (2026.acl-long)
Copied to clipboard
Yuxia Wang, Rui Xing, Jonibek Mansurov, Giovanni Puccetti, Zhuohan Xie, Minh Ngoc Ta, Jiahui Geng, Jinyan Su, Mervat Abassy, Saadeldine Eletter, Kareem Elozeiri, Nurkhan Laiyk, Maiya Goloburda, Tarek Mahmoud, Raj Vardhan Tomar, Alexander Aziz, Ryuto Koike, Masahiro Kaneko, Artem Shelmanov, Ekaterina Artemova, Vladislav Mikhailov, Akim Tsvigun, Alham Fikri Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov
| Challenge: | Prior studies have shown that distinguishing text generated by Large Language Models from human-written text is challenging for humans and often no better than random guessing. |
| Approach: | They conduct extensive case study to determine the upper bound of human detection accuracy. |
| Outcome: | The findings challenge previous conclusions on human detection accuracy across languages and domains. |