To completely understand a document, the use of textual information is not enough. Understanding visual cues, such as layouts and charts, is also required. While the current state-of-the-art approaches for document understanding (both OCR-based and OCR-free) work well, a thorough analysis of their capabilities and limitations has not yet been performed. Therefore, in this work, we addresses the limitation of current VisualQA models when applied to charts and plots. To investigate shortcomings of the state-of-the-art models, we conduct a comprehensive behavioral analysis, using ChartQA as a case study. Our findings indicate that existing models particularly underperform in answering questions related to the chart's structural and visual context, as well as numerical information. To address these issues, we propose three simple pre-training tasks that enforce the existing model in terms of both structural-visual knowledge, as well as its understanding of numerical questions. We evaluate our pre-trained model (called MatCha-v2) on three chart datasets - both extractive and abstractive question datasets - and observe that it achieves an average improvement of 1.7% over the baseline model.
ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering
York University（约克大学）；Dialpad Inc.（Dialpad公司）；RBC（加拿大皇家银行）；MILA - Quebec AI Institute（米拉-魁北克人工智能研究所）；Qatar Computing Research Institute (QCRI)（卡塔尔计算研究所）；Nanyang Technological University（南洋理工大学）；Salesforce Research（Salesforce研究院）
ChartReasoner: Code-Driven Modality Bridging for Long-Chain Reasoning in Chart Question Answering
MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所MAIS研究所）；Shenyang Institute of Computing Technology, Chinese Academy of Sciences（中国科学院沈阳计算技术研究所）；Beijing Wenge Technology Co., Ltd.（北京文耕科技有限公司）；University of Chinese Academy of Sciences（中国科学院大学）
DashboardQA: Benchmarking Multimodal Agents for Question Answering on Interactive Dashboards
York University（约克大学）；RBC；Qatar Computing Research Institute (QCRI)（卡塔尔计算研究所）；Nanyang Technological University（南洋理工大学）；Salesforce Research（Salesforce研究）
ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering
Beijing Wenge Technology Co., Ltd.（北京文格科技有限公司）；Shenyang Institute of Computing Technology, Chinese Academy of Sciences（沈阳计算技术研究所，中国科学院）；MAIS, Institute of Automation, Chinese Academy of Sciences（自动化研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）
Enhancing Financial Report Question-Answering: A Retrieval-Augmented Generation System with Reranking Analysis
School of Engineering Stanford University Stanford, CA, USA；Simon Business School University of Rochester Rochester, NY, USA；Accounting \& Information Systems Rutgers University Newark, NJ, USA；Institute for Social；Economic Research；Policy Columbia University New York, NY, USA；Department of Economics Northeastern University Boston, MA, USA
IIE-NLP-Eyas at SemEval-2021 Task 4: Enhancing PLM for ReCAM with Special Tokens, Re-Ranking, Siamese Encoders and Back Translation
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
PaniniQA: Enhancing Patient Education Through Interactive Question Answering
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Emory University（埃默里大学）；Northeastern University（东北大学）；UMass Chan Medical School（马萨诸塞大学陈医学院）；University of Minnesota（明尼苏达大学）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）
EvidenceLens: A Claim-Evidence Matrix for Auditing Financial Question Answering
1 School of AI；Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, China 2 ETSI de Telecomunicaci\' o n, Universidad Polit\' e cnica de Madrid, Madrid, Spain IEEE VIS 2026 conditionally accepted version
JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capability
Manning College of Information and Computer Sciences, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校信息与计算机科学学院）；Department of Medicine, University of Massachusetts Medical School（马萨诸塞大学医学院医学系）；Miner School of Computer and Information Sciences, University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校计算机与信息科学学院）；Center for Healthcare Organization and Implementation Research, VA Bedford Health Care（弗吉尼亚州贝德福德医疗保健系统医疗保健组织与实施研究中心）
Enhancing textual textbook question answering with large language models and retrieval augmented generation
Faculty of Computing and Information Technology, King Abdulaziz University（阿卜杜勒阿齐兹国王大学计算与信息技术学院）；College of Computer and Information Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)（伊玛目穆罕默德·本·沙特伊斯兰大学计算机与信息科学学院）；School of Computing, Macquarie University（麦考瑞大学计算学院）
MedLogic-AQA: Enhancing Medical Question Answering with Abstractive Models Focusing on Logical Structures
School of AI and Data Science, Indian Institute of Technology Jodhpur（印度焦特布尔印度理工学院人工智能与数据科学学院）；Department of Computer Science and Engineering, Indian Institute of Technology Patna（印度巴特那印度理工学院计算机科学与工程系）
Confident RAG: Enhancing the Performance of LLMs for Mathematics Question Answering through Multi-Embedding and Confidence Scoring
Faculty of Education, The University of Hong Kong（香港大学教育学院）；Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology（香港科学与技术大学土木与环境工程系）
