Papers by Thanh-Thien Le
SharpSeq: Empowering Continual Event Detection through Sharpness-Aware Sequential-task Learning (2024.naacl-long)
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| Challenge: | Existing methods for continual event detection suffer from catastrophic forgetting . a novel continual learning paradigm leveraging sharpness-aware minimization is needed . |
| Approach: | They propose a continual learning paradigm that leverages sharpness-aware minimization and a generative model to balance training data distribution. |
| Outcome: | The proposed approach outperforms existing methods on real-world datasets. |
Realistic Evaluation of Toxicity in Large Language Models (2024.findings-acl)
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| Challenge: | a large amount of data exposes large language models to toxicity and bias . prompt engineering can be easily bypassed with minimal prompt engineering. |
| Approach: | They propose a dataset that uses manually crafted prompts to nullify protective layers of large language models. |
| Outcome: | The proposed dataset shows that prompts can nullify protective layers of large language models. |
Lifelong Event Detection via Optimal Transport (2024.emnlp-main)
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| Challenge: | Continual event detection (CED) is a challenging task due to catastrophic forgetting, where learning new tasks hampers performance on previous ones. |
| Approach: | They propose a method that leverages optimal transport principles to align the optimization of a classification module with the intrinsic nature of each class, as defined by their pre-trained language modeling. |
| Outcome: | The proposed method outperforms state-of-the-art methods on MAVEN and ACE datasets and is a pioneering solution in continual event detection. |
ToVo: Toxicity Taxonomy via Voting (2025.findings-naacl)
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Tinh Son Luong, Thanh-Thien Le, Thang Viet Doan, Linh Ngo Van, Thien Huu Nguyen, Nguyen Thi Ngoc Diep
| Challenge: | Existing toxic content detection models face limitations due to the closed-source nature of training data and the paucity of explanations for their evaluation mechanism. |
| Approach: | They propose a mechanism that integrates voting and chain-of-thought processes to produce a high-quality open-source dataset for toxic content detection. |
| Outcome: | The proposed model improves transparency and customizability while facilitating better fine-tuning for specific use cases. |