Papers by Shaowei Wang
Diagram-Driven Course Questions Generation (2025.emnlp-main)
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Xinyu Zhang, Lingling Zhang, Yanrui Wu, Muye Huang, Wenjun Wu, Bo Li, Shaowei Wang, Basura Fernando, Jun Liu
| Challenge: | Visual Question Generation (VQG) research focuses on natural images while neglecting diagrams, a critical component of educational materials. |
| Approach: | They propose a diagram-driven course questions generation task to generate diagram-relevant questions for specific courses. |
| Outcome: | The proposed framework outperforms existing models on DiagramQG while maintaining strong generalizability across natural image datasets. |
From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan (2026.acl-long)
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Lei Yang, Leiyu Pan, Bojian Xiong, Renren Jin, Shaowei Zhang, Yue Chen, Ling Shi, Jiang Zhou, Junru Wu, Zhen Wang, Jianxiang Peng, Juesi Xiao, Tianyu Dong, Zhuowen Han, Zhuo Chen, Yuqi Ren, Deyi Xiong
| Challenge: | Large language models have achieved remarkable success across a wide range of tasks, yet their performance remains heavily biased toward high-resource languages. |
| Approach: | They propose a pipeline for advancing Tibetan language modeling through multilingual continual pre-training with Tibetan, Chinese, and English. |
| Outcome: | The proposed model outperforms open-source and Tibetan-focused models on diverse tasks. |
PBI-Attack: Prior-Guided Bimodal Interactive Black-Box Jailbreak Attack for Toxicity Maximization (2025.emnlp-main)
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Ruoxi Cheng, Yizhong Ding, Shuirong Cao, Ranjie Duan, Xiaoshuang Jia, Shaowei Yuan, Simeng Qin, Zhiqiang Wang, Xiaojun Jia
| Challenge: | Existing methods to jailbreak Large Vision Language Models do not consider interaction between images and text. |
| Approach: | They propose a prior-guided bimodal interactive black-box jailbreak attack for toxicity maximization that exploits the interaction of images and text. |
| Outcome: | The proposed method outperforms state-of-the-art jailbreak methods in black box scenarios and in closed-source LVLMs. |
CODEPROMPTZIP: Code-specific Prompt Compression for Retrieval-Augmented Generation in Coding Tasks with LMs (2026.findings-acl)
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| Challenge: | Existing prompt compression techniques for natural language lack fine-grained control over compression ratios. |
| Approach: | They propose a code-aware prompt compression framework for RAG that enables precise length control while preserving critical information. |
| Outcome: | The proposed framework outperforms baselines on three code-related tasks while maintaining the most informative tokens. |
Model Performance-Guided Evaluation Data Selection for Effective Prompt Optimization (2025.findings-acl)
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| Challenge: | Existing prompt engineering methods rely on randomly selected evaluation subsets, leading to suboptimal prompts. |
| Approach: | They propose an iterative evaluation data selection approach for effective prompt optimization using real time model performance. |
| Outcome: | The proposed approach improves effectiveness by 1.6% to 3.1% and stability by 50% to 55.5% on two datasets BIG-bench and LIAR and two models GPT-3.5 and GPT-4o-mini. |
Synchronous Double-channel Recurrent Network for Aspect-Opinion Pair Extraction (2020.acl-main)
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| Challenge: | Existing studies focus on aspect-opinion relation detection, but neglect to recognize the relations between aspects and opinion expressions. |
| Approach: | They propose a Synchronous Double-channel Recurrent Network to deal with AOPE task . they propose an opinion entity extraction unit, a relation detection unit, and a synchronization unit . |
| Outcome: | The proposed system achieves state-of-the-art in opinion entity extraction . it is based on three datasets based upon SemEval 2014 and 2015 benchmarks . |
Heterogeneous Graph Transformer for Graph-to-Sequence Learning (2020.acl-main)
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| Challenge: | Recent studies ignore the indirect relations between distance nodes, or treat indirect relations and direct relations in the same way. |
| Approach: | They propose a graph-to-sequence (Graph2Seq) encoder which models graph structure to model different relations in individual subgraphs of the original graph. |
| Outcome: | The proposed model outperforms the state-of-the-art on all four benchmarks of AMR-to-text generation and syntax-based neural machine translation. |
SLICEFORMER: Static Program Slicing Using Language Models With Dataflow-Aware Pretraining and Constrained Decoding (2026.acl-long)
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| Challenge: | Static program slicing is a software engineering technique for isolating code relevant to specific variables. |
| Approach: | They propose a new approach that reformulates static program slicing as a sequence-to-sequence task using small language models such as CodeT5+. |
| Outcome: | The proposed approach improves on Java and Python program slicing benchmarks with up to 22% gain in ExactMatch. |