Input errors in question-answering (QA) systems often lead to incorrect responses. Large language models (LLMs) struggle with this task, frequently failing to interpret user intent (misinterpretation) or unnecessarily altering the original question's structure (over-correction). We propose QuestionRAG, a framework that tackles these problems. To address misinterpretation, it enriches the input with external knowledge (e.g., search results, related entities). To prevent over-correction, it uses reinforcement learning (RL) to align the model's objective with precise correction, not just paraphrasing. Our results demonstrate that knowledge augmentation is critical for understanding faulty questions. Furthermore, RL-based alignment proves significantly more effective than traditional supervised fine-tuning (SFT), boosting the model's ability to follow instructions and generalize. By integrating these two strategies, QuestionRAG unlocks the full potential of LLMs for the question correction task.
RAIR: Retrieval-Augmented Iterative Refinement for Chinese Spelling Correction
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Zhongke Fanyu Technology Co., Ltd（中科泛语科技有限公司）
Do "New Snow Tablets" Contain Snow? Large Language Models Over-Rely on Names to Identify Ingredients of Chinese Drugs
Liaoning University（辽宁大学）；University of Queensland（昆士兰大学）；National University of Singapore（新加坡国立大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of California, Merced（加利福尼亚大学默塞德分校）
Inspired by a concept of content-addressable retrieval from cognitive science, we propose a novel fragment-based model augmented with a lexicon-based memory for Chinese NER, in which both the character-level and word-level features are combined to generate better feature representations for possible name candidates. It is observed that locating the boundary information of entity names is useful in order to classify them into pre-defined categories. Position-dependent features, including prefix and suffix are introduced for NER in the form of distributed representation. The lexicon-based memory…
LegalMALR:Multi-Agent Query Understanding and LLM-Based Reranking for Chinese Statute Retrieval
Faculty of Data Science（数据科学学院）；City University of Macau（澳门城市大学）；Shenzhen Key Laboratory for High Performance Data Mining（深圳高性能数据挖掘重点实验室）；Shenzhen Institutes of Advanced Technology（深圳先进技术研究所）；Chinese Academy of Sciences（中国科学院）；Southern University of Science and Technology（南方科技大学）；Artificial Intelligence Research Institute（人工智能研究院）；Shenzhen University of Advanced Technology（深圳大学先进技术学院）
CHILLGuard: Towards Fine-Grained Chinese LLM Safety Guardrail with Scalable Data Construction and Model-aware Preference Alignment
Tsinghua University（清华大学）；Beijing Normal University（北京师范大学）；South China University of Technology（华南理工大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Shenzhen ShenNong Information Technology Co., Ltd.（深圳神农信息技术有限公司）
University of Waterloo（滑铁卢大学）；Penn State University（宾夕法尼亚州立大学）
In-context learning using large language models has recently shown surprising results for semantic parsing tasks such as Text-to-SQL translation. Prompting GPT-3 or Codex using several examples of question-SQL pairs can produce excellent results, comparable to state-of-the-art finetuning-based models. However, existing work primarily focuses on English datasets, and it is unknown whether large language models can serve as competitive semantic parsers for other languages. To bridge this gap, our work focuses on cross-lingual Text-to-SQL semantic parsing for translating non-English utterances in…
Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategies
Shanghai Institute of Infectious Disease and Biosecurity, Fudan University（复旦大学附属上海市感染与生物安全研究院）；Shanghai Health Development Research Center(Shanghai Medical Information Center)（上海卫生发展研究中心（上海市医学信息中心））；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
To investigate the role of linguistic knowledge in data augmentation (DA) for Natural Language Processing (NLP), we designed two adapted DA programs and applied them to LCQMC (a Large-scale Chinese Question Matching Corpus) for a binary Chinese question matching classification task. The two DA programs produce augmented texts by five simple text editing operations (or DA techniques), largely irrespective of language generation rules, but one is enhanced with a pre-trained n-gram language model to fuse it with prior linguistic knowledge. We then trained four neural network models (BOW, CNN, LST…
Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing Inducements
Provable Responsible AI and Data Analytics (PRADA) Lab（可证责任AI与数据分析实验室）；King Abdullah University of Science and Technology（卡瓦尔大学科学与技术大学）；University of Toronto（多伦多大学）；University of Macau（澳门大学）；University of Electronic Science and Technology of China（电子科技大学）
TCM-DiffRAG: Personalized Syndrome Differentiation Reasoning Method for Traditional Chinese Medicine based on Knowledge Graph and Chain of Thought
Macao Polytechnic University（澳门理工大学）；Guangdong Institute of Intelligence Science and Technology（广东智能科学技术研究院）；Zhejiang Chinese Medical University（浙江中医药大学）；Hangzhou Ganzhicao Technology Co., Ltd（杭州甘之草科技有限公司）；BoardWare Information System Limited（BoardWare信息系统有限公司）
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）
ZhiFangDanTai: Fine-tuning Graph-based Retrieval-Augmented Generation Model for Traditional Chinese Medicine Formula
School of Management, Capital Normal University（管理学院，首都师范大学）；School of Electrical Engineering and Computer Science, the University of Queensland（电气工程与计算机科学学院，昆士兰大学）
CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models
University of Science and Technology of China（中国科学技术大学）；Institute for Advanced Algorithms Research (Shanghai)（（上海）高级算法研究院）；Renmin University of China（中国人民大学）；AI Research Institute（360人工智能研究院）
New Terms, New Toxicity: Consensus-based Chinese Neologism Toxicity Detection via Search-Augmented LLMs
Tsinghua University（清华大学）；ICT, CAS（中国科学院计算技术研究所）；National Computer Network Emergency Response Technical Team Coordination Center of China（国家计算机网络应急技术处理协调中心）；IIE, CAS（中国科学院工程热物理研究所）；School of Cyber Security, UCAS（中国科学院大学网络空间安全学院）；JCSS, Tsinghua University（清华大学软件学院）；Science City (Guangzhou) Digital Technology Group Co., Ltd.（广州科学城数字科技集团有限公司）
HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs Responses
Key Laboratory of High Confidence Software Technologies (Peking University)（高可信软件技术重点实验室（北京大学））；School of Computer Science, Peking University（北京大学计算机学院）
FoodSky: A Food-oriented Large Language Model that Passes the Chef and Dietetic Examination
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Intelligent Computing Technology, Chinese Academy of Sciences（中国科学院智能计算技术研究所）；Zhongke Shenjian (Suzhou) Technology Co., Ltd（中科神箭（苏州）科技有限公司）
KBM: Delineating Knowledge Boundary for Adaptive Retrieval in Large Language Models
College of Software, Nankai University（南开大学软件学院）；Tongyi Lab, Alibaba Group（阿里巴巴集团 Tongyi 实验室）；School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）
Shanghai Jiao Tong University（上海交通大学）；The University of Melbourne（墨尔本大学）
Compared with English, Chinese suffers from more grammatical ambiguities, like fuzzy word boundaries and polysemous words. In this case, contextual information is not sufficient to support Chinese named entity recognition (NER), especially for rare and emerging named entities. Semantic augmentation using external knowledge is a potential way to alleviate this problem, while how to obtain and leverage external knowledge for the NER task remains a challenge. In this paper, we propose a neural-based approach to perform semantic augmentation using external knowledge from search engine for Chinese …
ManuSearch: Democratizing Deep Search in Large Language Models with a Transparent and Open Multi-Agent Framework
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学人工智能学院）；Harbin Institute of Technology（哈尔滨工业大学）；Department of Computer Science, National University of Singapore（新加坡国立大学计算机科学系）
LLMs Can Also Do Well! Breaking Barriers in Semantic Role Labeling via Large Language Models
Institute of Computing and Intelligence, Harbin Institute of Technology (Shenzhen)（计算与智能研究院，哈尔滨工业大学（深圳））；School of Electrical Engineering and Automation, Harbin Institute of Technology（电气工程与自动化学院，哈尔滨工业大学）；Computer Science Department, Harbin Institute of Technology (Shenzhen)（计算机科学系，哈尔滨工业大学（深圳））
In this paper, we develop a new multi-singer Chinese neural singing voice synthesis (SVS) system named WeSinger. To improve the accuracy and naturalness of synthesized singing voice, we design several specifical modules and techniques: 1) A deep bi-directional LSTM-based duration model with multi-scale rhythm loss and post-processing step; 2) A Transformer-alike acoustic model with progressive pitch-weighted decoder loss; 3) a 24 kHz pitch-aware LPCNet neural vocoder to produce high-quality singing waveforms; 4) A novel data augmentation method with multi-singer pre-training for stronger robus…
