Papers by Quan Du
Weight Distillation: Transferring the Knowledge in Neural Network Parameters (2021.acl-long)
Copied to clipboard
| Challenge: | Knowledge distillation is an effective method for model acceleration and compression. |
| Approach: | They propose to use parameters to distill knowledge from large neural networks to small ones . they propose to do this by using a parameter generator to transfer the knowledge to a small neural network . |
| Outcome: | The proposed method learns a small network 1.88 2.94x faster than the large network but with competitive BLEU points. |
A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction (2020.coling-main)
Copied to clipboard
| Challenge: | Recent work has questioned the robustness of unsupervised bilingual dictionary induction methods on distant language pairs. |
| Approach: | They propose an iterative dimension reduction method to bridge this gap . they propose a method that initializes and self-learning and inducing a dictionary . |
| Outcome: | The proposed method achieves 13.64 55.53% accuracy between English and four distant languages. |
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation (2022.acl-long)
Copied to clipboard
Bei Li, Quan Du, Tao Zhou, Yi Jing, Shuhan Zhou, Xin Zeng, Tong Xiao, JingBo Zhu, Xuebo Liu, Min Zhang
| Challenge: | Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE). |
| Approach: | They propose a residual block of layers in Transformer that can be described as a higher-order solution to ODE. |
| Outcome: | The proposed architecture can gain large improvements over strong baselines at a slight cost in inference efficiency. |
Shallow-to-Deep Training for Neural Machine Translation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Experimental results show that deep training is 1:4 faster than training from scratch. |
| Approach: | They propose a shallow-to-deep training method that learns deep models by stacking shallow models. |
| Outcome: | The proposed method is 1:4 faster than training from scratch and achieves BLEU scores of 30:33 and 43:29 on two translation tasks. |
NiuTrans.LMT: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs (2026.acl-long)
Copied to clipboard
Yingfeng Luo, Ziqiang Xu, Yuxuan Ouyang, MuRun Yang, DingYang Lin, Kaiyan Chang, Tong Zheng, Bei Li, Peinan Feng, Quan Du, Tong Xiao, JingBo Zhu
| Challenge: | Large language models have significantly advanced Multilingual Machine Translation (MMT) yet scaling to many languages while maintaining robust performance across directions remains challenging. |
| Approach: | They propose a strategy to reduce the number of translations in one direction . they propose auxiliary parallel sentences to promote cross-lingual transfer . |
| Outcome: | The proposed model performs on par with or better than substantially larger baselines. |
Step Potential Advantage Estimation: Harnessing Intermediate Confidence and Correctness for Efficient Mathematical Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to RLVR provide sparse supervision since reward arrives only after the full generation is complete. |
| Approach: | They propose a step-level reward system that extracts confidence and correctness and combines them into a Step Potential signal that explicitly estimates reasoning state at each step. |
| Outcome: | The proposed method outperforms existing methods on multiple benchmarks and improves accuracy while reducing response length. |
RouteLMT: Learned Sample Routing for Hybrid LLM Translation Deployment (2026.acl-industry)
Copied to clipboard
Yingfeng Luo, Hongyu Liu, DingYang Lin, Kaiyan Chang, Chenglong Wang, Bei Li, Quan Du, Tong Xiao, JingBo Zhu
| Challenge: | Existing routing strategies rely on heuristics, external predictors, or absolute quality estimation to capture whether the large model provides a worthwhile improvement over the small one. |
| Approach: | They propose a budget allocation problem for routing large model to large model . they propose heuristics, external predictors, or absolute quality estimation to determine the optimal signal for budgeted decisions. |
| Outcome: | The proposed model outperforms heuristics, quality/difficulty estimation baselines and achieves a superior quality–budget Pareto frontier. |