Papers by Caiwen Ding
Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning (2020.findings-emnlp)
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| Challenge: | Pretrained large-scale language models have been criticized for their limited weight storage and computational speed on hardware platforms. |
| Approach: | They propose an efficient transformer-based large-scale language representation using hardware-friendly block structure pruning. |
| Outcome: | The proposed model achieves 5.0x accuracy on GLUE benchmarks and 1.79x compression rate on DistilBERT. |
TAG: Gradient Attack on Transformer-based Language Models (2021.findings-emnlp)
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Jieren Deng, Yijue Wang, Ji Li, Chenghong Wang, Chao Shang, Hang Liu, Sanguthevar Rajasekaran, Caiwen Ding
| Challenge: | Recent studies show that publicly shared gradients in the training process can reveal the private training data to a third-party. |
| Approach: | They propose a gradient attack algorithm to reconstruct the local training data using GLUE benchmarks. |
| Outcome: | The proposed algorithm achieves 1.5x recover rate and 2.5x ROUGE-2 over previous methods without the need of ground truth label. |
A Secure and Efficient Federated Learning Framework for NLP (2021.emnlp-main)
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Chenghong Wang, Jieren Deng, Xianrui Meng, Yijue Wang, Ji Li, Sheng Lin, Shuo Han, Fei Miao, Sanguthevar Rajasekaran, Caiwen Ding
| Challenge: | Existing FL frameworks require a trusted aggregator or require heavy-weight cryptographic primitives, which makes the performance significantly degraded. |
| Approach: | They propose a framework that is federated and efficient for NLP . they propose to eliminate the need for trusted entities and achieve better model accuracy . |
| Outcome: | The proposed framework achieves better model accuracy and model accuracy than existing FL frameworks. |
Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation (2026.acl-long)
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Le Chen, Nuo Xu, Winson Chen, Bin Lei, Pei-Hung Lin, Dunzhi Zhou, Rajeev Thakur, Caiwen Ding, Ali Jannesari, Chunhua Liao
| Challenge: | Large language models (LLMs) have shown remarkable capabilities in code translation, yet their performance deteriorates in low-resource programming domains such as Fortran and emerging frameworks like CUDA . |
| Approach: | They propose a dual-LLM Questioner–Solver pipeline that integrates external knowledge from compilers and runtime feedback to generate verified translations and multi-turn dialogues. |
| Outcome: | The proposed model outperforms proprietary models on key metrics like compilation success and accuracy. |
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm (2022.acl-long)
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Shaoyi Huang, Dongkuan Xu, Ian Yen, Yijue Wang, Sung-En Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Sanguthevar Rajasekaran, Hang Liu, Caiwen Ding
| Challenge: | Conventional wisdom in pruning Transformer-based language models is that it reduces model expressiveness, but new research shows pruning increases risk of overfitting when performed at the fine-tuning phase. |
| Approach: | They propose to reduce pruning risk under pretrain-and-finetune paradigm . they propose to use knowledge distillation to improve pruning performance . |
| Outcome: | The proposed method outperforms the leading competitors on the GLUE benchmark. |