Papers by Binh Nguyen
Task-driven Layerwise Additive Activation Intervention (2025.naacl-short)
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| Challenge: | Existing approaches to task adaptation rely heavily on heuristic rules or prompt inputs. |
| Approach: | They propose a layer-wise additive activation intervention framework that steers the LMs’ generation process by identifying and manipulating the activations. |
| Outcome: | The proposed framework improves the accuracy of pretrained LMs and competing baselines on various datasets, demonstrating improvements in the accuracy and sample efficiency of the proposed framework. |
Multi-level Community-awareness Graph Neural Networks for Neural Machine Translation (2022.coling-1)
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| Challenge: | Recent studies have used Graph Neural Networks (GNNs) to encode language knowledge into token embeddings. |
| Approach: | They propose a multi-level community-awareness Graph Neural Network layer to jointly model local and global relationships between words and their linguistic roles in multiple communities. |
| Outcome: | The proposed method reduces time complexity in very long sentences while preserving the original meaning. |
Distributional Surgery for Language Model Activations (2025.findings-emnlp)
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| Challenge: | Language models can produce undesirable outputs including harmful or toxic outputs. |
| Approach: | They propose a method to detect undesirable content using activations . they propose layerwise distributional steering policies that transform the attention heads . |
| Outcome: | The proposed method outperforms baselines in reducing undesirable output generation. |
What You Read Isn’t What You Hear: Linguistic Sensitivity in Deepfake Speech Detection (2025.emnlp-main)
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| Challenge: | Recent advances in text-to-speech technology have enabled highly realistic voice generation, fueling deepfake attacks. |
| Approach: | They propose a framework for transcript-to-audio perturbation anti-spoofing that incorporates linguistic variation into detectors to investigate spoof detection. |
| Outcome: | The proposed framework can bypass commercial detectors by incorporating linguistic variation into the design of anti-spoofing systems. |
HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts (2023.emnlp-main)
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Truong Do, Le Khiem, Quang Pham, TrungTin Nguyen, Thanh-Nam Doan, Binh Nguyen, Chenghao Liu, Savitha Ramasamy, Xiaoli Li, Steven Hoi
| Challenge: | Recent studies suggest that fixing the routers can achieve competitive performance by alleviating the collapsing problem, where all experts eventually learn similar representations. |
| Approach: | They propose a method that dynamically generates router parameters through a fixed hypernetwork and trainable embeddings to achieve a balance between training the routers and freezing them to learn an improved routing policy. |
| Outcome: | Experiments on a wide range of tasks show that the proposed method performs better than existing methods. |