Papers by Yanxu Chen
How Do Your Code LLMs perform? Empowering Code Instruction Tuning with Really Good Data (2024.emnlp-main)
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Yejie Wang, Keqing He, Dayuan Fu, Zhuoma GongQue, Heyang Xu, Yanxu Chen, Zhexu Wang, Yujia Fu, Guanting Dong, Muxi Diao, Jingang Wang, Mengdi Zhang, Xunliang Cai, Weiran Xu
| Challenge: | Recent research has shown that code pre-trained models improve coding capabilities. |
| Approach: | They propose a code data pruning strategy to identify which datasets are high-quality code instruction data. |
| Outcome: | The proposed model achieves state-of-the-art performance using fewer training data. |
BMCook: A Task-agnostic Compression Toolkit for Big Models (2022.emnlp-demos)
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Zhengyan Zhang, Baitao Gong, Yingfa Chen, Xu Han, Guoyang Zeng, Weilin Zhao, Yanxu Chen, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing efforts to compress medium-sized models for specific tasks have limited results. |
| Approach: | They propose a task-agnostic compression toolkit for big models that implements quantization, pruning, distillation and MoEfication methods. |
| Outcome: | The proposed tool improves performance on a model with 3 billion parameters by 12x . it also outperforms the original model on three typical NLP benchmarks. |