Papers by Cen Wang
ReFSQL: A Retrieval-Augmentation Framework for Text-to-SQL Generation (2023.findings-emnlp)
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Kun Zhang, Xiexiong Lin, Yuanzhuo Wang, Xin Zhang, Fei Sun, Cen Jianhe, Hexiang Tan, Xuhui Jiang, Huawei Shen
| Challenge: | Existing methods that align natural language with SQL Language underestimate inherent structural characteristics of SQL and lead to structure errors. |
| Approach: | They propose a retrieval-argument framework that aligns natural language with SQL Language and trains one encoder-decoder-based model to fit all questions. |
| Outcome: | The proposed framework improves accuracy and robustness of text-to-SQL generation on five datasets. |
PAI-Diffusion: Constructing and Serving a Family of Open Chinese Diffusion Models for Text-to-image Synthesis on the Cloud (2024.acl-demos)
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| Challenge: | Existing diffusion models fail to address the challenges of generating high-quality images from textual descriptions due to its large vocabulary size and complex character relationships. |
| Approach: | They propose a framework that integrates Chinese diffusion models with Alibaba Cloud's Platform for AI and enables the generation of contextually relevant images. |
| Outcome: | The proposed framework integrates with Alibaba Cloud’s Platform for AI, providing accessible and scalable solutions. |
PrivacyRestore: Privacy-Preserving Inference in Large Language Models via Privacy Removal and Restoration (2025.acl-long)
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| Challenge: | Existing privacy protection methods for large language models suffer from performance degradation or large inference time overhead. |
| Approach: | They propose a plug-and-play method to protect the privacy of user inputs during LLM inference . they use offline restoration vectors to train restoration vector for each privacy span type . |
| Outcome: | The proposed method can prevent the linear growth of the privacy budget. |
LongRAG: A Dual-Perspective Retrieval-Augmented Generation Paradigm for Long-Context Question Answering (2024.emnlp-main)
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| Challenge: | Existing long-context Large Language Models (LLMs) struggle with the “lost in the middle” issue. |
| Approach: | They propose a general, dual-perspective, and robust LLM-based RAG system paradigm for LCQA to enhance RAG’s understanding of complex long-context knowledge. |
| Outcome: | The proposed system outperforms long-context LLMs, advanced RAG, and vanilla RAG on three multi-hop datasets. |
STORM-BORN: A Challenging Mathematical Derivations Dataset Curated via a Human-in-the-Loop Multi-Agent Framework (2025.findings-acl)
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Wenhao Liu, Zhenyi Lu, Xinyu Hu, Jerry Zhang, Dailin Li, Jiacheng Cen, Huilin Cao, Haiteng Wang, Yuhan Li, Xie Kun, Dandan Li, Pei Zhang, Chengbo Zhang, Yuxiang Ren, Xiaohong Huang, Yan Ma
| Challenge: | Existing datasets suffer from outdated and insufficient challenging content, neglecting human-like reasoning, and limited reliability due to single-LLM generation. |
| Approach: | They propose a human-in-the-loop, multi-agent data generation framework that integrates reasoning-dense filters, multiagent collaboration, and human mathematicians’ evaluations to ensure the reliability and quality of the dataset. |
| Outcome: | The proposed framework improves accuracy and quality of the 2,000-synthesized datasets by integrating reasoning-dense filters, multi-agent collaboration, and human mathematicians’ evaluations. |
A Customized Text Sanitization Mechanism with Differential Privacy (2023.findings-acl)
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| Challenge: | Existing methods to sanitize texts subject to differential privacy do not work for non-metric semantic similarity measures. |
| Approach: | They propose a customized text sanitization mechanism based on a metric local differential privacy definition. |
| Outcome: | The proposed mechanism achieves better privacy-utility trade-offs than existing mechanisms on benchmark datasets. |
Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding (2023.emnlp-main)
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Taolin Zhang, Ruyao Xu, Chengyu Wang, Zhongjie Duan, Cen Chen, Minghui Qiu, Dawei Cheng, Xiaofeng He, Weining Qian
| Challenge: | Existing methods for pre-training KEPLMs with relational triples are difficult to adapt to close domains due to the lack of sufficient domain graph semantics. |
| Approach: | They propose a Knowledge-enhanced language representation learning framework for various closed domains that captures the implicit graph structure among the entities. |
| Outcome: | The proposed framework outperforms existing methods for pre-training KEPLMs in closed domains significantly. |
TaiChi: Improving the Robustness of NLP Models by Seeking Common Ground While Reserving Differences (2024.lrec-main)
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| Challenge: | Pre-trained Language Models are vulnerable to adversarial examples that introduce human-imperceptible perturbations to clean examples to deceive the models. |
| Approach: | They propose a Siamese network-based approach to teach adversarial models to focus on similarities . they propose combining two sub-networks sharing the same structure but trained on clean and adversarials . |
| Outcome: | The proposed approach reduces the differences between clean and adversarial samples and focuses more on similarities. |
RewardDS: Privacy-Preserving Fine-Tuning for Large Language Models via Reward Driven Data Synthesis (2025.emnlp-main)
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Jianwei Wang, Chengming Shi, Junyao Yang, Haoran Li, Qianli Ma, Huiping Zhuang, Cen Chen, Ziqian Zeng
| Challenge: | Existing solutions to fine-tune large language models for domain-specific tasks are ineffective in addressing privacy concerns. |
| Approach: | They propose a privacy-preserving framework that fine-tunes a reward proxy model and uses reward signals to guide the synthetic data generation. |
| Outcome: | The proposed framework fine-tunes a reward proxy model and uses reward signals to guide the synthetic data generation. |
Rapid Diffusion: Building Domain-Specific Text-to-Image Synthesizers with Fast Inference Speed (2023.acl-industry)
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Bingyan Liu, Weifeng Lin, Zhongjie Duan, Chengyu Wang, Wu Ziheng, Zhang Zipeng, Kui Jia, Lianwen Jin, Cen Chen, Jun Huang
| Challenge: | Text-to-Image Synthesis (TIS) aims to generate images based on textual inputs . but, current diffusion-based models lack entity knowledge and low inference speed . |
| Approach: | They propose a framework for training and deploying latent diffusion models with rich entity knowledge injected and optimized networks. |
| Outcome: | The proposed framework improves image quality and inference speed and can be used in industrial applications. |
AscendKernelGen: LLM-Driven Kernel Generation for NPUs (2026.findings-acl)
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Xinzi Cao, Jianyang Zhai, Pengfei Li, Zhiheng Hu, Cen Yan, null Mubingxu, Guanghuan Fang, Bin She, Jiayu Li, Yihan Su, Dongyang Tao, Feidiao Yang, Chang-Dong Wang, Yutong Lu, Weicheng Xue, Bin Zhou, Yonghong Tian
| Challenge: | Neural Processing Units (NPUs) are critical for AI infrastructure, but their development remains a bottleneck due to vendor-specific Domain-Specific Languages (DSLs). |
| Approach: | They propose a framework for NPU kernel development that bridges the gap in hardware-specific coding . compiler success on complex Level-2 kernels improves from 0% to 95.5%, they say . |
| Outcome: | The proposed framework bridges the gap in hardware-specific coding, showing a near-zero success rate on complex kernels. |
A Siamese CNN Architecture for Learning Chinese Sentence Similarity (2020.aacl-srw)
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| Challenge: | a deep neural architecture is used to learn a semantic similarity metric between two sentences . traditional methods of learning sentence similarity are based on the word level, which may not be sufficient. |
| Approach: | They propose a deep neural architecture which uses siamese convolutional neural network sharing model parameters to learn a semantic similarity metric between two sentences. |
| Outcome: | The proposed architecture outperforms baselines in similarity metrics for Chinese sentences by 8.7 points. |
LayerConnect: Hypernetwork-Assisted Inter-Layer Connector to Enhance Parameter Efficiency (2022.coling-1)
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| Challenge: | Existing parameter-efficient methods focus on reducing trainable parameters but neglect the inference speed, which limits the ability to deploy PLMs. |
| Approach: | They propose to use a hypernetwork-assisted inter-layer connector to enhance inference efficiency by tuning parameters inside a linear connector between two Transformer layers. |
| Outcome: | The proposed model reduces model parameters to 11.75% while preserving performance degradation to less than 5%. |
XtremeCLIP: Extremely Parameter-efficient Tuning for Low-resource Vision Language Understanding (2023.findings-acl)
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| Challenge: | Existing approaches to fine-tune visual-language understanding (VLU) require tasks-specific designs and sufficient training data. |
| Approach: | They propose a simple yet efficient paradigm for low-resource Visual Language Understanding (VLU) they reformulate a series of VLU tasks as an open-book affinity-matching problem. |
| Outcome: | The proposed framework outperforms baselines in low-resource settings. |
MedCoach: Enhancing Medical Reasoning in LLMs via Knowledge Graph-Augmented Chain-of-Thought Distillation (2026.findings-acl)
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| Challenge: | Existing methods for training specialized reasoning models for the medical domain are limited due to the scarcity of high-quality, large-scale Chain-of-Thought (CoT) data. |
| Approach: | They propose a framework that introduces a dedicated coach role to guide the student model through question decomposition. |
| Outcome: | The proposed framework smooths the learning curve in medical reasoning by facilitating domain adaptation before advancing to complex long-chain reasoning. |
Privacy Evaluation Benchmarks for NLP Models (2024.findings-emnlp)
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| Challenge: | Several kinds of privacy attacks are studied in depth, but they are non-systematic and lack a comprehensive understanding of the impact caused by the attacks. |
| Approach: | They propose a privacy attack and defense evaluation benchmark in the field of NLP . they propose an improved attack method and a chained framework for privacy attacks . |
| Outcome: | The proposed framework can be chained to achieve a higher-level attack objective. |