Machine Reading Comprehension (MRC) aims to extract answers to questions given a passage. It has been widely studied recently, especially in open domains. However, few efforts have been made on closed-domain MRC, mainly due to the lack of large-scale training data. In this paper, we introduce a multi-target MRC task for the medical domain, whose goal is to predict answers to medical questions and the corresponding support sentences from medical information sources simultaneously, in order to ensure the high reliability of medical knowledge serving. A high-quality dataset is manually constructed for the purpose, named Multi-task Chinese Medical MRC dataset (CMedMRC), with detailed analysis conducted. We further propose the Chinese medical BERT model for the task (CMedBERT), which fuses medical knowledge into pre-trained language models by the dynamic fusion mechanism of heterogeneous features and the multi-task learning strategy. Experiments show that CMedBERT consistently outperforms strong baselines by fusing context-aware and knowledge-aware token representations.
Fùxì: A Benchmark for Evaluating Language Models on Ancient Chinese Text Understanding and Generation
School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；Lab of Artificial Intelligence for Education, East China Normal University（华东师范大学教育人工智能实验室）；Shanghai Institute of Artificial Intelligence for Education, East China Normal University（华东师范大学上海教育人工智能研究院）
Can ChatGPT Really Understand Modern Chinese Poetry?
NLP；CT Lab, Department of Computer and Information Science, University of Macau（计算机与信息科学系，澳门科技大学CT实验室）；Department of Portuguese, Faculty of Arts and Humanities, University of Macau（人文学院葡萄牙语系，澳门科技大学）
Large Language Models Empowered Personalized Web Agents
National University of Singapore（新加坡国立大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Science and Technology of China（中国科学技术大学）；Eastern Institute of Technology（宁波东方理工大学）
Towards Multimodal Metaphor Understanding: A Chinese Dataset and Model for Metaphor Mapping Identification
Dalian University of Technology（大连理工大学）；School of Foreign Languages（外国语学院）；School of Software（软件学院）；University of Macau（澳门大学）；Faculty of Business Administration（工商管理学院）；Kansai University（关西大学）；Faculty of Business and Commerce（商学部）；University of New South Wales（新南威尔士大学）；School of Minerals and Energy Resources Engineering（矿产与能源资源工程学院）；RMIT University（皇家墨尔本理工大学）；Centre for Educational Innovation and Quality（教育创新与质量中心）；School of Computing Technologies（计算技术学院）
Beyond Reproduction: A Paired-Task Framework for Assessing LLM Comprehension and Creativity in Literary Translation
Natural Language Learning Group (NLLG) School of Business Informatics and Mathematics University of Mannheim（曼海姆大学自然语言学习组（NLLG）商学院信息与数学学院）；University of Technology Nuremberg (UTN), Department Engineering（纽伦堡技术大学（UTN）工程系）；University of Gent, Department of Translation, Interpreting and Communication（根特大学翻译、口译与传播系）；University of Aberdeen, Department of Computing Science（阿伯丁大学计算科学系）；Natural Language Learning and Generation (NLLG) Lab（自然语言学习与生成（NLLG）实验室）
Do Large Language Models Perform Well on Comprehending Poetic Logic in Modern Chinese Poetry?
Graduate School of Informatics, Kyoto University（京都大学信息学研究科）；University of Macau（澳门大学）；College of Computer Science, Inner Mongolia University（内蒙古大学计算机学院）
Consensus Attention-based Neural Networks for Chinese Reading Comprehension
iFLYTEK Research（科大讯飞研究）；Research Center for Social Computing and Information Retrieval,Harbin Institute of Technology（哈尔滨工业大学社会计算与信息检索研究中心）；Harbin Institute of Technology（哈尔滨工业大学）
Native Chinese Reader: A Dataset Towards Native-Level Chinese Machine Reading Comprehension
IIIS, Tsinghua University（清华大学交叉信息研究院）；New York University（纽约大学）；Haihua Institute for Frontier Information Technology（海华前沿信息技术研究院）；Shenzhen University（深圳大学）；Peking University（北京大学）；Shanghai Qi Zhi Institute（上海期智研究院）
我们提出了Native Chinese Reader（NCR），一个新的机器阅读理解（MRC）数据集，特别是现代和古典汉语的长文章。NCR是从中国高中语文课程的考试试题中收集的，旨在评估中国本土青年的语言能力。现有的中文MRC数据集要么是特定领域的，要么只关注现代汉语中几百个字符的短上下文。相比之下，NCR包含8390个文档，平均长度为1024个字符，涵盖了广泛的中文写作风格，包括现代文章，古典文学和古典诗歌。这些文件上总共有20477道题，也需要很强的推理能力和常识才能算出正确答案。我们使用流行的中国预训练模型实现了多个基线模型，并使用我们的数据集发起了一场在线竞赛，以检查当前方法的局限性。最好的模型达到了59%的测试准确率，而人工评估的平均准确率为79%，这表明当前MRC模型与母语为汉语的人之间存在显着的性能差距。我们在https://sites.google.com/view/native-chinese-reader/上发布数据集。
Large Language Model Empowered Recommendation Meets All-domain Continual Pre-Training
National University of Singapore（新加坡国立大学）；Singapore Management University（新加坡管理大学）；Tencent（腾讯）；Shandong University（山东大学）；City, University London（伦敦城市大学）
A Sentence Cloze Dataset for Chinese Machine Reading Comprehension
Research Center for SCIR, Harbin Institute of Technology（哈尔滨工业大学SCIR研究中心）；State Key Laboratory of Cognitive Intelligence, iFLYTEK Research, China（科大讯飞研究中心认知智能国家重点实验室）；iFLYTEK AI Research (Hebei), Langfang, China（科大讯飞（河北）人工智能研究院，廊坊）
HKCanto-Eval: A Benchmark for Evaluating Cantonese Language Understanding and Cultural Comprehension in LLMs
Kyushu University（九州大学）；hon9kon9ize；The Education University of Hong Kong（香港教育大学）；The University of Hong Kong（香港大学）；Independent Researcher（独立研究者）；Votee AI；Beever AI
Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hong Kong University of Science and Technology（香港科技大学）；IDEA（深圳国际数字经济研究院）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
Modelling dependency completion in sentence comprehension as a Bayesian hierarchical mixture process: A case study involving Chinese relative clauses
University of Potsdam（波茨坦大学）；École Nationale de la Statistique et de l’administration économique（国家统计与经济管理学院）；CNRS（法国国家科学研究中心）；Université Paris-Dauphine（巴黎九大）；PSL Research University（巴黎文理研究大学）
ZPD-SCA: Unveiling the Blind Spots of LLMs in Assessing Students' Cognitive Abilities
School of Psychology, South China Normal University（南方科技大学心理学院）；Information Hub, Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）信息中心）；School of AI, Guangzhou University（广州大学人工智能学院）；College of Cyber Security, Jinan University（济南大学网络安全学院）
ChID: A Large-scale Chinese IDiom Dataset for Cloze Test
Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Nanyang Technological University（南洋理工大学）
Underutilization of Syntactic Processing by Chinese Learners of English in Comprehending English Sentences, Evidenced from Adapted Garden-Path Ambiguity Experiment
Can Large Language Models Understand Internet Buzzwords Through User-Generated Content
College of Computer Science, Sichuan University, China（四川大学计算机学院）；Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education, China（教育部机器学习与工业智能工程研究中心）；JD.com, China（京东公司）
Vision-Language Intelligence: Tasks, Representation Learning, and Large Models
The Hong Kong University of Science and Technology（香港科技大学）；Chinese Academy of Science（中国科学院）；International Digital Economy Academy (IDEA)（国际数字经济研究院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Microsoft Research（微软研究院）；Tsinghua University（清华大学）
RAM2C: A Liberal Arts Educational Chatbot based on Retrieval-augmented Multi-role Multi-expert Collaboration
Center for Brain Inspired Computing Research (CBICR), Tsinghua University（清华大学脑启发计算研究中心）；Optical Memory National Engineering Research Center, Tsinghua University（清华大学光存储国家工程研究中心）；Tsinghua University-China Electronics Technology HIK Group Co. Joint Research Center for Brain-inspired Computing（清华大学-中国电子 hik 集团联合脑启发计算研究中心）；IDG/McGovern Institute for Brain Research, Tsinghua University（清华大学 IDG/麦戈文脑科学研究院）；Tsinghua University（清华大学）
