Papers by Shu Wei
MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering (2025.findings-acl)
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Jingqun Tang, Qi Liu, Yongjie Ye, Jinghui Lu, Shu Wei, An-Lan Wang, Chunhui Lin, Hao Feng, Zhen Zhao, Yanjie Wang, Yuliang Liu, Hao Liu, Xiang Bai, Can Huang
| Challenge: | Text-Centric Visual Question Answering (TEC-VQA) is a text-centric visual task understanding tool. |
| Approach: | They introduce a benchmark that features human expert annotations across 9 languages . they prioritize the text in question-answer pairs while disregarding visual text in images . |
| Outcome: | The proposed benchmarks prioritize the text in question-answer pairs while disregarding visual text in images. |
EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification (2024.findings-acl)
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| Challenge: | Existing studies on fact verification lack a high-quality dataset for explainability . existing systems lack evidence retrieval and veracity prediction, limiting the ability to verify a claim . |
| Approach: | They propose a dataset for multi-hop explainable fact verification that summarises and modifies Wikipedia documents. |
| Outcome: | The proposed dataset aims to improve the accuracy of multi-hop explainable fact verification systems. |
M3TQA: Massively Multilingual Multitask Table Question Answering (2026.findings-acl)
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Daixin Shu, Jian Yang, Zhenhe Wu, Xianjie Wu, Xianfu Cheng, Guan Xiangyuan, Yanghai Wang, Pengfei Wu, Tingyang Yang, Hualei Zhu, Wei Zhang, Ge Zhang, Jiaheng Liu, Zhoujun Li
| Challenge: | Existing multilingual table benchmarks suffer from geolinguistic imbalance - overrepresenting certain languages and lacking sufficient scale for rigorous cross-lingual analysis. |
| Approach: | They propose a framework for massively multilingual table question answering that includes tables expanded to 97 languages from Chinese and English sources. |
| Outcome: | Experiments on state-of-the-art LLMs show that synthetically generated training data significantly boosts performance, especially for low-resource languages. |
PAFT: Prompt-Agnostic Fine-Tuning (2025.emnlp-main)
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| Challenge: | Prompt-agnostic fine-tuning (PAFT) improves performance by reducing overfitting to specific prompts. |
| Approach: | They propose a method that enhances robustness through dynamic prompt variation during training. |
| Outcome: | The proposed method achieves higher generalization accuracy on unseen prompts than standard methods with similar training efficiency. |
Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluation (2025.findings-emnlp)
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Tong Li, Shu Yang, Junchao Wu, Jiyao Wei, Lijie Hu, Mengdi Li, Derek F. Wong, Joshua R. Oltmanns, Di Wang
| Challenge: | Existing data on suicidal ideation in private conversations are limited . a new dataset of 1,200 test cases is presented to address this gap . |
| Approach: | They propose a dataset of 1,200 test cases simulating implicit suicidal ideation in private contexts. |
| Outcome: | The proposed dataset includes 1,200 test cases simulating implicit suicidal ideation in dialogue scenarios. |
CompTab: A Comprehensive Benchmark for Real-World TableQA with Complex Reasoning and Irregular Tables (2026.acl-long)
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Zhen Yang, Wei Du, Jie Wang, Wenze Zhou, Xiangfeng Meng, Zhengyang Wang, Suping Sun, Ziwei Du, Haodong Zou, Jie Chen, Yongbin Liu, Shicheng Tan, Jiahao Ying, Shu Zhao
| Challenge: | Existing benchmarks focus on well-structured tables and fail to reflect irregular structures and complex reasoning commonly encountered in real-world scenarios. |
| Approach: | They propose a benchmark to evaluate TableQA under complex reasoning and irregular table conditions. |
| Outcome: | The proposed framework improves generalization and realism of large language models under complex and irregular table conditions. |
M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions (2024.acl-long)
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| Challenge: | Existing methods for retrieving relevant memories from an external database are coarse-grained and can cause noise and focus on crucial memories. |
| Approach: | They propose a multiple partition paradigm for RAG where each database partition serves as a basic unit for execution. |
| Outcome: | The proposed framework outperforms baseline methods on three language generation tasks on seven datasets. |
Flexora: Flexible Low-Rank Adaptation for Large Language Models (2025.acl-long)
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| Challenge: | Large language models (LLMs) have revolutionized artificial intelligence, but performance on specific tasks is limited by knowledge boundaries. |
| Approach: | They propose a method that automatically selects the most critical layers for fine-tuning to optimize performance across diverse downstream tasks. |
| Outcome: | The proposed method outperforms baseline models and natural language tasks. |
Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting (2025.findings-acl)
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Hao Feng, Shu Wei, Xiang Fei, Wei Shi, Yingdong Han, Lei Liao, Jinghui Lu, Binghong Wu, Qi Liu, Chunhui Lin, Jingqun Tang, Hao Liu, Can Huang
| Challenge: | Current document image parsing solutions rely on specialized models or generate content autoregressively. |
| Approach: | They propose a multimodal document image parsing model that integrates specialized models with autogeneous content generation. |
| Outcome: | The proposed model achieves state-of-the-art performance across diverse page-level and element-level settings while ensuring superior efficiency. |