Papers by Chris Ngo
Selective Steering: Norm-Preserving Control Through Discriminative Layer Selection (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for inference-time steering are limited by their limitations . Angular Steering violates norm preservation, causing distribution shift and generation collapse . |
| Approach: | They propose a method that uses a norm-preserving rotation formulation to maintain activation distribution integrity and discriminative layer selection to apply steering only where features exhibit opposite-signed class alignment. |
| Outcome: | Experiments show that Selective Steering achieves higher attack success rates than prior methods while maintaining zero perplexity violations and approximately 100% capability retention on standard benchmarks. |
SilVar: Speech-Driven Multimodal Model for Reasoning Visual Question Answering and Object Localization (2025.emnlp-main)
Copied to clipboard
| Challenge: | Visual Language Models have demonstrated remarkable capabilities across various tasks, including visual question answering and image captioning. |
| Approach: | They propose an end-to-end multimodal model that leverages speech instructions for reasoning-based visual question answering. |
| Outcome: | The proposed model can process and explain visual scenes from spoken input, moving beyond simple object recognition to reasoning-based interactions. |
MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation (2025.emnlp-main)
Copied to clipboard
Khai Le-Duc, Tuyen Tran, Bach Phan Tat, Nguyen Kim Hai Bui, Quan Dang Anh, Hung-Phong Tran, Thanh Thuy Nguyen, Ly Nguyen, Tuan Minh Phan, Thi Thu Phuong Tran, Chris Ngo, Khanh Xuan Nguyen, Thanh Nguyen-Tang
| Challenge: | Multilingual speech translation (ST) and machine translation (MT) in the medical domain enhances patient care by enabling efficient communication across language barriers. |
| Approach: | They present a large-scale ST dataset for the medical domain spanning all translation directions in Vietnamese, English, German, French, and Simplified/Traditional Chinese, together with the models. |
| Outcome: | The multi-language speech translation (ST) and machine translation (MT) in the medical domain is the largest medical MT dataset and the largest many-to-many multilingual ST among all domains. |