LET: Linguistic Knowledge Enhanced Graph Transformer for Chinese Short Text Matching
State Key Laboratory of Media Convergence Production Technology and Systems（媒体融合生产技术与系统国家重点实验室）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院教育部人工智能重点实验室）
Chinese short text matching is a fundamental task in natural language processing. Existing approaches usually take Chinese characters or words as input tokens. They have two limitations: 1) Some Chinese words are polysemous, and semantic information is not fully utilized. 2) Some models suffer potential issues caused by word segmentation. Here we introduce HowNet as an external knowledge base and propose a Linguistic knowledge Enhanced graph Transformer (LET) to deal with word ambiguity. Additionally, we adopt the word lattice graph as input to maintain multi-granularity information. Our model is also complementary to pre-trained language models. Experimental results on two Chinese datasets show that our models outperform various typical text matching approaches. Ablation study also indicates that both semantic information and multi-granularity information are important for text matching modeling.
A GAN and LLM-Driven Data Augmentation Framework for Dynamic Linguistic Pattern Modeling in Chinese Sarcasm Detection
School of Cyber Science and Engineering, Sichuan University（四川大学网络空间安全学院）；State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University（浙江大学流体动力与机电系统国家重点实验室）；Law School, Sichuan University（四川大学法学院）
Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference
Indiana University Bloomington（印第安纳大学伯明顿分校）；Brigham Young University（杨百翰大学）；Northwestern University（西北大学）；UNC Chapel Hill（北卡罗来纳大学教堂山分校）；Allen Institute for AI（艾伦人工智能研究所）；Shanghai Jiaotong Univ.（上海交通大学）
SLING: Sino Linguistic Evaluation of Large Language Models
UMass Amherst（马萨诸塞大学阿默斯特分校）
为了了解预训练的汉语语言模型（LM）编码了哪些语言知识，我们引入了汉语语言学（SLING）基准，它由38 K最小句子对组成，分为9个高级语言现象。每一对都展示了特定句法或语义现象的可接受性对比（例如，关键字丢失vs.关键字丢失），LM应该为可接受的句子分配较低的困惑度。与CLiMP数据集（Xiang et al.，2021），其中也包含中文最小对，并通过翻译英语BLiMP数据集的词汇创建，SLING中的最小对主要是通过对中文树库9.0中自然发生的语言学家注释的句子应用句法和词汇转换来获得的，从而解决了CLiMP数据生成过程中的严重问题。我们测试了18个公开的预训练单语（例如，BERT-base-zh，CPM）和多语言（例如，mT5，XLM）SLING上的语言模型。我们的实验表明，LM的平均准确率远远低于人类的表现（69.7%对97.1%），而BERT-base-zh在所有测试的LM中达到了最高的准确率（84.8%），甚至更大。此外，我们发现，大多数LM有很强的性别和数量（单/复数）的偏见，他们表现得更好的地方比层次的现象。
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）
How Much Do LLMs Know About Chinese Zero Pronouns?
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning（湖北省人工智能与智能学习重点实验室）；National Language Resources Monitoring and Research Center for Network Media（网络媒体语言资源监测与研究中心）；School of Computer Science, Central China Normal University（中央财经大学计算机学院）
Chinese Essay Rhetoric Recognition Using LoRA, In-context Learning and Model Ensemble
The Open University of China（中国开放大学）；Engineering Research Center of Integration and Application of Digital Learning Technology, Ministry of Education（教育部数字学习技术整合与应用工程研究中心）
CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models
HKUST（香港科技大学）；Queen Mary University of London（伦敦大学玛丽皇后学院）；Ohio State University（俄亥俄州立大学）；AIWaves Inc.（AIWaves公司）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, Chinese Academy of Sciences（中国科学院人工智能学院）；Peking University（北京大学）；Beijing Foreign Studies University（北京外国语大学）；University of Manchester（曼彻斯特大学）；University of Waterloo（滑铁卢大学）；National University of Singapore（新加坡国立大学）；Zhejiang University（浙江大学）；Beihang University（北京航空航天大学）
Improving Pre-trained Language Models with Syntactic Dependency Prediction Task for Chinese Semantic Error Recognition
Harbin Institute of Technology（哈尔滨工业大学）；iFLYTEK Research（讯飞研究院）
现有的中文文本错误检测主要集中在拼写和简单的语法错误。这些错误已被广泛研究，对人类来说相对简单。相反，汉语的语义错误研究不足，而且更加复杂，人类不容易识别。本文的任务是汉语语义错误识别（CSER），一个二元分类任务，以确定一个句子是否包含语义错误。目前的研究还没有有效的方法来解决这个问题。本文继承了BERT的模型结构，设计了多个与句法相关的预训练任务，使模型能够学习句法知识。我们的预训练任务考虑依赖结构的方向性和依赖关系的多样性。由于目前尚缺乏针对CSER的公开数据集，本文首次建立了一个高质量的CSER数据集--汉语语义可接受性语料库（Corpus of Chinese Linguistic Semantic Acceptability，CoCLSA）。在CoCLSA上的实验结果表明，我们的方法优于通用的预训练模型和语法注入模型。
Probing Social Identity Bias in Chinese LLMs with Gendered Pronouns and Social Groups
Department of Electronics, Information and Bioengineering, Politecnico di Milano（电子、信息与生物工程系，米兰理工大学）；University of Science and Technology of China（中国科学技术大学）
A Large-Scale Chinese Knowledge Graph-Text Alignment Dataset for Benchmarking Knowledge-Grounded LLMs
Nanjing University of Science and Techonolgy（南京理工大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；University of Science and Technology Beijing（北京科技大学）；Hefei University of Technology（合肥工业大学）；Beijing University of Chemical Technology（北京化工大学）；Renmin University of China（中国人民大学）；Beihang University（北航）；Beijing University of Posts and Telecommunications（北京邮电大学）；Griffith University, Australia（澳大利亚格里菲斯大学）
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning
Shenzhen Institute of Advanced Technology, CAS（中国科学院深圳先进技术研究院）；M-A-P；Institute of Automation, CAS（中国科学院自动化研究所）；Peking University（北京大学）；University of Science and Technology of China（中国科学技术大学）；HKUST（香港科技大学）；University of Manchester（曼彻斯特大学）；University of Waterloo（滑铁卢大学）；Vector Institute（向量研究所）
The Role of Dataset Linguistic Structure in the Cultural Awareness of Large Language Models
University College London（伦敦大学学院）；Queen’s University Belfast（贝尔法斯特女王大学）；The University of Tokyo（东京大学）；University of Bisha（比沙大学）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；AI Centre, University College London（伦敦大学学院人工智能中心）
Evaluating Chinese Ambiguity Understanding in Large Language Models
Graduate School of Information Science and Technology, The University of Tokyo（东京大学信息科学与技术研究生院）；School of Software Engineering, South China University of Technology（华南理工大学软件学院）
TCM-3CEval: A Triaxial Benchmark for Assessing Responses from Large Language Models in Traditional Chinese Medicine
School of Acupuncture-Moxibustion and Tuina, Shanghai University of Traditional Chinese Medicine（上海中医药大学针灸推拿学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；China Academy of Chinese Medical Sciences（中国中医科学院）
LinguaLens: Towards Interpreting Linguistic Mechanisms of Large Language Models via Sparse Auto-Encoder
DCST, BNRist（DCST、BNRist；KIRC、人工智能研究院、清华大学，中国）；KIRC, Institute for Artificial Intelligence, Tsinghua University, China（中文语言文学系、北京大学，中国）；Department of Chinese Language and Literature, Peking University, China
Who Wrote This Line? Evaluating the Detection of LLM-Generated Classical Chinese Poetry
College of Computer Science, Inner Mongolia University, China（内蒙古大学计算机学院，中国）；National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian, China（蒙古语智能信息处理技术国家与地方联合工程研究中心，中国）；NLP²CT Lab, Department of Computer and Information Science, University of Macau, China（澳门大学计算机与信息科学学院NLP²CT实验室，中国）
