Papers by Yi Tu
LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding (2023.acl-long)
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| Challenge: | Pre-trained models on document images with transformer-based backbones have led to significant performance gains in this field. |
| Approach: | They propose a multi-modal pre-training model that combines text, layout and image . they propose to use local 1D position instead of global 1D positions as layout input . |
| Outcome: | The proposed model can achieve state-of-the-art results on a wide variety of VrDU problems. |
Crossing the Reward Bridge: Expanding Reinforcement Learning with Verifiable Rewards Across Diverse Domains (2026.acl-long)
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| Challenge: | Reinforcement learning with verifiable rewards (RLVR) has been effective on structured tasks, but its reliance on simple, rule-based verifiers creates a bottleneck. |
| Approach: | They propose a framework that uses a generative verifier to provide soft, probabilistic rewards. |
| Outcome: | The proposed framework outperforms existing models up to 10x their size and can be scalable and effective. |
Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction (2023.emnlp-main)
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| Challenge: | Recent advances in multimodal pre-trained models have significantly improved information extraction from visually-rich documents (VrDs). |
| Approach: | They propose a method to predict token sequences within visually-rich documents by a simple prediction head. |
| Outcome: | The proposed method can be used to predict token mentions as token sequences within documents. |
Too Good to be Bad: On the Failure of LLMs to Role-Play Villains (2026.findings-acl)
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Zihao Yi, Qingxuan Jiang, Ruotian Ma, Xingyu Chen, Qu Yang, Mengru Wang, Fanghua Ye, Ying Shen, Zhaopeng Tu, Xiaolong Li, Liefeng Bo
| Challenge: | Large Language Models (LLMs) are increasingly tasked with creative generation, but their ability to portray non-prosocial, antagonistic personas remains largely unexamined. |
| Approach: | They propose a moral alignment benchmark to test the safety of large language models . they find that models struggle with traits directly antithetical to safety principles . |
| Outcome: | The proposed model fails to accurately portray morally ambiguous or villainous characters . the model fails most with traits directly antithetical to safety principles . |
Change Entity-guided Heterogeneous Representation Disentangling for Change Captioning (2025.findings-acl)
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| Challenge: | Existing approaches to describe differences between two images are highly challenging due to distractors such as illumination and viewpoint changes. |
| Approach: | They propose a change-entity-guided disentanglement network that explicitly learns difference representations while mitigating the impact of distractors. |
| Outcome: | The proposed method outperforms existing methods on CLEVR-Change, CLE VR-DC and Spot-the-Diff datasets and achieves state-of-the art performance. |
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation (2021.naacl-demos)
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Qingyun Wang, Manling Li, Xuan Wang, Nikolaus Parulian, Guangxing Han, Jiawei Ma, Jingxuan Tu, Ying Lin, Ranran Haoran Zhang, Weili Liu, Aabhas Chauhan, Yingjun Guan, Bangzheng Li, Ruisong Li, Xiangchen Song, Yi Fung, Heng Ji, Jiawei Han, Shih-Fu Chang, James Pustejovsky, Jasmine Rah, David Liem, Ahmed ELsayed, Martha Palmer, Clare Voss, Cynthia Schneider, Boyan Onyshkevych
| Challenge: | a new framework to digest relevant biomedical knowledge is needed to combat COVID-19 . quantity of research results is a bottleneck, and false information promoted in publications . |
| Approach: | a team of researchers has developed a framework to extract multimedia knowledge elements from scientific literature to combat COVID-19. |
| Outcome: | a new framework extracts fine-grained multimedia knowledge elements from scientific literature . it provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence . the framework is based on a case study of drug repurposing . |
An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering (D19-58)
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| Challenge: | XLNet model is domain-agnostic for the MRQA 2019 Shared Task . a negative sampling technique is particularly effective for datasets that include unanswerable questions . |
| Approach: | They develop a domain-agnostic question answering model for the MRQA 2019 Shared Task . they use large pre-trained language models, various data sampling strategies and query and context paraphrases generated by back-translation . |
| Outcome: | The proposed model achieves second best Exact Match and F1 in the MRQA leaderboard competition. |
SAFETY-J: Evaluating Safety with Critique (2024.findings-emnlp)
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| Challenge: | Current methods focus on binary safety classifications and lack detailed critique, limiting their utility for model improvement and user trust. |
| Approach: | They propose a bilingual generative safety evaluator for English and Chinese with critique-based judgment that utilizes a robust training dataset and augmented query-response pairs to assess safety across various scenarios comprehensively. |
| Outcome: | The proposed model improves safety evaluations by assessing the quality of critiques with minimal human intervention. |
Unveiling the Deficiencies of Pre-trained Text-and-Layout Models in Real-world Visually-rich Document Information Extraction (2026.findings-eacl)
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Chong Zhang, Yixi Zhao, Yulu Xie, Chenshu Yuan, Yi Tu, Ya Guo, Mingxu Chai, Ziyu Shen, Yue Zhang, Qi Zhang
| Challenge: | PTLMs have shown remarkable success in multiple information extraction tasks . however, their performance in real-world scenarios falls short of expectations . |
| Approach: | They propose to use an entity-centric dataset to evaluate PTLMs' performance . they find that inadequate annotations in benchmark datasets lead to spurious correlations . |
| Outcome: | The proposed dataset disentangles the falsely-coupled segment and entity annotations that arises from the block-level annotation of FUNSD. |
Modeling Layout Reading Order as Ordering Relations for Visually-rich Document Understanding (2024.emnlp-main)
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Chong Zhang, Yi Tu, Yixi Zhao, Chenshu Yuan, Huan Chen, Yue Zhang, Mingxu Chai, Ya Guo, Huijia Zhu, Qi Zhang, Tao Gui
| Challenge: | Existing models of layout reading order do not convey the complete reading order information in the layout. |
| Approach: | They propose to model layout reading order as ordering relations over layout elements . they propose a reading-order-relation-enhancing pipeline to improve model performance . |
| Outcome: | The proposed model outperforms existing models on a visual-rich document dataset and on eight cross-domain VrD-IE/QA tasks without targeted optimization. |
SLIM: Subtrajectory-Level Elimination for More Effective Reasoning (2025.findings-emnlp)
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Xifeng Yao, Chengyuan Ma, Dongyu Lang, Yinhao Ni, Zhiwei Xu, Huarui Xie, Zihao Chen, Guang Shen, Dandan Tu, Yi Bai, Changzheng Zhang
| Challenge: | Notable examples include OpenAI’s o1/o3/o4 series and DeepSeek-R1 . |
| Approach: | They develop a framework to identify suboptimal subtrajectories based on human-established criteria . they also use a sampling algorithm to select data whose reasoning process is free from suboptimally subtravertories to the highest degree . |
| Outcome: | The proposed method reduces the number of suboptimal subtrajectories by 25.9% during the inference process. |
TOREE: Evaluating Topic Relevance of Student Essays for Chinese Primary and Middle School Education (2024.findings-acl)
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Xinlin Zhuang, Hongyi Wu, Xinshu Shen, Peimin Yu, Gaowei Yi, Xinhao Chen, Tu Hu, Yang Chen, Yupei Ren, Yadong Zhang, Youqi Song, Binxuan Liu, Man Lan
| Challenge: | Existing research on Automatic Essay Scoring (AES) for Chinese essays has overlooked topic relevance and lacks detailed feedback. |
| Approach: | They propose to use TOREE to assess topic relevance in Chinese primary and middle school students’ essays to improve automatic and human evaluations. |
| Outcome: | The proposed method significantly improves both automatic and human evaluations across four diverse LLMs. |
Conic10K: A Challenging Math Problem Understanding and Reasoning Dataset (2023.findings-emnlp)
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| Challenge: | Existing benchmarks or datasets require only a few steps of reasoning, making it difficult to analyse AI’s behaviour with reference to different problems within a specific topic in detail. |
| Approach: | They propose a conic10K math problem dataset that requires only a few steps of reasoning to be analysed. |
| Outcome: | The proposed dataset shows that existing language models exhibit weak performance on complex reasoning. |