Papers by Yitao Xu
The Illusion of Specialization: Unveiling the Domain-Invariant "Standing Committee" in Mixture-of-Experts Models (2026.acl-long)
Copied to clipboard
| Challenge: | Mixture of Experts models are widely assumed to achieve domain specialization through sparse routing. |
| Approach: | They propose a framework that analyzes routing behavior at the level of expert groups rather than individual experts. |
| Outcome: | The proposed framework analyzes routing behavior at the level of expert groups rather than individual experts. |
RBPtool: A Deep Language Model Framework for Multi-Resolution RBP-RNA Binding Prediction and RNA Molecule Design (2025.emnlp-main)
Copied to clipboard
Jiyue Jiang, Yitao Xu, Zikang Wang, Yihan Ye, Yanruisheng Shao, Yuheng Shan, Jiuming Wang, Xiaodan Fan, Jiao Yuan, Yu Li
| Challenge: | RNA-binding proteins play key roles in post-transcriptional gene regulation . existing methods focus on shallow sequence features or coarse structural representations . large language models allow for precise modeling and biologically informed de novo RNA design . |
| Approach: | They extend RPI15223 into a multi-resolution, structure-level RBP-RNA dataset and introduce RBPtool, a framework that fuses sequence and structural information. |
| Outcome: | The proposed framework achieves state-of-the-art performance on public benchmarks and the RPI15223 dataset while supporting fine-grained level predictions. |
Adaptive Gating in Mixture-of-Experts based Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing models employ a fixed gating network where each token is computed by the same number of experts. |
| Approach: | They propose a flexible training strategy that allows tokens to be processed by a variable number of experts based on expert probability distribution. |
| Outcome: | The proposed model reduces training time and inference quality while maintaining sparsity while maintaining inference accuracy. |
A Paradigm Shift: The Future of Machine Translation Lies with Large Language Models (2024.lrec-main)
Copied to clipboard
Chenyang Lyu, Zefeng Du, Jitao Xu, Yitao Duan, Minghao Wu, Teresa Lynn, Alham Fikri Aji, Derek F. Wong, Longyue Wang
| Challenge: | Large Language Models (LLMs) are introducing a new phase in machine translation . despite advances in MT, there are still many challenges to overcome . |
| Approach: | They propose to highlight several new directions for MT that are influenced by Large Language Models like GPT-4 and ChatGPT. |
| Outcome: | The proposed models offer vast linguistic understandings and bring innovative methodologies, such as prompt-based techniques, that have the potential to further elevate MT. |