Papers by Yongxin Yu
Recurrent Knowledge Identification and Fusion for Language Model Continual Learning (2025.acl-long)
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Yujie Feng, Xujia Wang, Zexin Lu, Shenghong Fu, Guangyuan Shi, Yongxin Xu, Yasha Wang, Philip S. Yu, Xu Chu, Xiao-Ming Wu
| Challenge: | Continual learning (CL) is crucial for large language models without costly retraining. |
| Approach: | They propose a framework for recurrent knowledge identification and fusion that enables dynamic estimation of parameter importance distributions to enhance knowledge transfer. |
| Outcome: | The proposed framework mitigates catastrophic forgetting and enhances knowledge transfer. |
Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusion (2022.findings-emnlp)
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| Challenge: | Existing work on dependency prior structure integration into pre-trained models is still unclear. |
| Approach: | They propose a dependency-based fusion attention paradigm which explicitly introduces dependency prior structure into pre-trained models and adaptively fuses it with semantic information. |
| Outcome: | The proposed model achieves state-of-the-art or competitive performance on 10 public datasets, demonstrating the benefits of adaptively fusing dependency structure in semantic matching task. |
Generative Pre-trained Speech Language Model with Efficient Hierarchical Transformer (2024.acl-long)
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| Challenge: | Experimental results indicate that GPST significantly outperforms the existing speech language models in terms of word error rate, speech quality, and speaker similarity. |
| Approach: | They propose a hierarchical transformer that quantizes audio waveforms into two distinct types of discrete speech representations and integrates them within a transformer architecture. |
| Outcome: | The proposed model outperforms existing speech language models in word error rate, speech quality, and speaker similarity. |
GeoEdit: Geometric Knowledge Editing for Large Language Models (2025.emnlp-main)
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Yujie Feng, Li-Ming Zhan, Zexin Lu, Yongxin Xu, Xu Chu, Yasha Wang, Jiannong Cao, Philip S. Yu, Xiao-Ming Wu
| Challenge: | Existing training-based model editing methods struggle to incorporate new knowledge while preserving unrelated general knowledge. |
| Approach: | They propose a framework that uses geometric relationships to differentiate between neurons associated with new knowledge updates and those related to general knowledge perturbations. |
| Outcome: | The proposed framework avoids updating neurons with directions approximately orthogonal to existing knowledge, thus preserving the model’s generalization ability. |