Papers by Zhiping Wang
A Survey on Efficient Large Language Model Training: From Data-centric Perspectives (2025.acl-long)
Copied to clipboard
Junyu Luo, Bohan Wu, Xiao Luo, Zhiping Xiao, Yiqiao Jin, Rong-Cheng Tu, Nan Yin, Yifan Wang, Jingyang Yuan, Wei Ju, Ming Zhang
| Challenge: | achieving data-efficient post-training of Large Language Models is a key research question. |
| Approach: | They propose a taxonomy of data-efficient LLM post-training methods from a data-centric perspective. |
| Outcome: | The proposed methods cover data selection, data quality enhancement, synthetic data generation, data distillation and compression, and self-evolving data ecosystems. |
Guiding Abstractive Dialogue Summarization with Content Planning (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for abstractive dialogue summarization struggle to maintain factual consistency between dialogue and summary. |
| Approach: | They propose a coarse-to-fine model for generating abstractive dialogue summaries and introduce a fact-aware reinforcement learning objective that improves the fact consistency between the dialogue and the generated summary. |
| Outcome: | The proposed model improves the quality of the generated summary, especially in coherence and consistency. |
How Do Large Language Models Perform in Dynamical System Modeling (2025.findings-naacl)
Copied to clipboard
| Challenge: | Recent data-driven methods often use graph neural networks (GNNs) to learn interactions between objects. |
| Approach: | They propose prompting techniques for dynamical system modeling and evaluate their performance . they find that large language models demonstrate competitive performance without training . |
| Outcome: | The proposed methods show competitive performance without training compared to state-of-the-art methods in dynamical system modeling. |
Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention (2025.acl-long)
Copied to clipboard
Jingyang Yuan, Huazuo Gao, Damai Dai, Junyu Luo, Liang Zhao, Zhengyan Zhang, Zhenda Xie, Yuxing Wei, Lean Wang, Zhiping Xiao, Yuqing Wang, Chong Ruan, Ming Zhang, Wenfeng Liang, Wangding Zeng
| Challenge: | Long-context modeling is crucial for next-generation language models, but high computational cost of standard attention mechanisms poses significant computational challenges. |
| Approach: | They propose a natively trained Sparse Attention mechanism that integrates algorithms with hardware-aligned optimizations to achieve efficient long-context modeling. |
| Outcome: | The proposed model maintains or exceeds Full Attention models across general benchmarks, long-context tasks, and instruction-based reasoning. |
Automatic Marketing Theme and Commodity Construction System for E-commerce (2023.emnlp-industry)
Copied to clipboard
| Challenge: | Existing recommendation system invites experts to write marketing themes and select relevant commodities, which suffer from difficulty in mass production, poor timeliness and low online indicators. |
| Approach: | They propose to use pretrained language model to generate marketing themes and commodity consistency module to select relevant commodities for the generative theme. |
| Outcome: | The proposed system can generate popular marketing themes and select relevant commodities automatically and improve theme online effectiveness compared with state-of-the-art methods. |