This paper explores the application of synthetic data in the post-OCR domain on multiple fronts by conducting experiments to assess the impact of data volume, augmentation, and synthetic data generation methods on model performance. Furthermore, we introduce a novel algorithm that leverages computer vision feature detection algorithms to calculate glyph similarity for constructing post-OCR synthetic data. Through experiments conducted across a variety of languages, including several low-resource ones, we demonstrate that models like ByT5 can significantly reduce Character Error Rates (CER) without the need for manually annotated data, and our proposed synthetic data generation method shows advantages over traditional methods, particularly in low-resource languages.
ChemVA: Advancing Large Language Models on Chemical Reaction Diagrams Understanding
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University（浙江大学杭州全球科学与技术创新中心）；Department of Chemistry, Fudan University（复旦大学化学系）
Systematic Exploration of Dialogue Summarization Approaches for Reproducibility, Comparative Assessment, and Methodological Innovations for Advancing Natural Language Processing in Abstractive Summarization
SRM Institute of Science and Technology（SRM科学技术学院）；SRM University, AP（SRM大学（安得拉邦））
Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens
AIML, University of Adelaide（AIML，阿德莱德大学）；Monash University（墨尔本大学）；Australian National University（澳大利亚国立大学）；Tiktok, Australia（Tiktok，澳大利亚）；Zhejiang University（浙江大学）
Annotating Errors in English Learners' Written Language Production: Advancing Automated Written Feedback Systems
Tohoku University, Sendai, Japan（东京东京大学）；RIKEN, Wako, Japan（日本理化学研究所）；ALTA Institute, Computer Laboratory, University of Cambridge, U.K（剑桥大学计算机实验室ALTA研究所）；INSA Lyon, Villeurbanne, France（里昂国立应用科学学院）；CNRS, LIS, Aix-Marseille University, Marseille, France（国家科学研究中心（CNRS））；MBZUAI, Abu Dhabi, United Arab Emirates（阿布扎赫德人工智能研究所）
The African Languages Lab: A Collaborative Approach to Advancing Low-Resource African NLP
University of California, Los Angeles（加州大学洛杉矶分校）；Georgia Institute of Technology（佐治亚理工学院）；University of Wisconsin - Madison（威斯康星大学麦迪逊分校）；University of Cape Coast（科克伍德大学）；Carleton University（卡尔顿大学）；Stetson University（史蒂文森大学）；Northwestern University in Qatar（卡塔尔西北大学）；Cornell University（康奈尔大学）；Soka University of America（美国立命大学）；Columbia University（哥伦比亚大学）
Evaluating Large Language Models on the Spanish Medical Intern Resident (MIR) Examination 2024/2025:A Comparative Analysis of Clinical Reasoning and Knowledge Application
Advancing Chinese biomedical text mining with community challenges
Sichuan University（四川大学）；West China Hospital（华西医院）；Tongji University（同济大学）；Shanghai Business School（上海商学院）；Takeda Co. Ltd.（武田制药有限公司）；Harbin Institute of Technology（哈尔滨工业大学）
