Papers by Huy Huu Nguyen
Lizard: An Efficient Linearization Framework for Large Language Models (2026.acl-long)
Copied to clipboard
Chien Van Nguyen, Huy Huu Nguyen, Ruiyi Zhang, Hanieh Deilamsalehy, Puneet Mathur, Viet Dac Lai, Haoliang Wang, Jayakumar Subramanian, Ryan A. Rossi, Trung Bui, Nikos Vlassis, Franck Dernoncourt, Thien Huu Nguyen
| Challenge: | Existing linearization frameworks that rely on softmax attention with quadratic time and memory complexity pose significant computational and memory bottlenecks for long-context applications. |
| Approach: | They propose a linearization framework that transforms pretrained Transformer-based Large Language Models into subquadratic architectures that closely approximate softmax attention while preserving model quality. |
| Outcome: | Experiments show that the proposed framework outperforms existing methods by 9.4 - 24.5 points on the 5-shot MMLU benchmark and demonstrating superior associative recall. |
Towards Fast and Accurate Modeling for Cross-Lingual Label Projection (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for label projection are inaccurate or slow for large-scale use. |
| Approach: | They propose to synthesize alignment sequence pairs and fine-tune an encoder model with span alignment objective while controlling data influence during training. |
| Outcome: | The proposed method outperforms state-of-the-art methods while maintaining fast inference speed across 50+ languages. |
Massively Multilingual Instruction-Following Information Extraction (2025.findings-acl)
Copied to clipboard
| Challenge: | Past literature on information extraction (IE) has focused on a few high-resource languages, hindering their applications on multilingual corpora. |
| Approach: | They propose a collection of data that unifies and standardizes instruction-following multilingual IE and introduce a structure-aware metric that captures partially matched spans. |
| Outcome: | The proposed framework standardizes and unifies 215 manually annotated datasets, covering 96 typologically diverse languages from 18 language families. |