In multi-label text classification (MLTC), each given document is associated with a set of correlated labels. To capture label correlations, previous classifier-chain and sequence-to-sequence models transform MLTC to a sequence prediction task. However, they tend to suffer from label order dependency, label combination over-fitting and error propagation problems. To address these problems, we introduce a novel approach with multi-task learning to enhance label correlation feedback. We first utilize a joint embedding (JE) mechanism to obtain the text and label representation simultaneously. In MLTC task, a document-label cross attention (CA) mechanism is adopted to generate a more discriminative document representation. Furthermore, we propose two auxiliary label co-occurrence prediction tasks to enhance label correlation learning: 1) Pairwise Label Co-occurrence Prediction (PLCP), and 2) Conditional Label Co-occurrence Prediction (CLCP). Experimental results on AAPD and RCV1-V2 datasets show that our method outperforms competitive baselines by a large margin. We analyze low-frequency label performance, label dependency, label combination diversity and coverage speed to show the effectiveness of our proposed method on label correlation learning.
Enhancing AI-Driven Education: Integrating Cognitive Frameworks, Linguistic Feedback Analysis, and Ethical Considerations for Improved Content Generation
Learning, Data and Robotics (LDR) ESIEA Lab（学习、数据与机器人（LDR）ESIEA实验室）；Department of Computer Engineering（计算机工程系）；Graduate School of Management and Innovation（管理与创新研究生院）；Faculty of Science（科学学院）；Lebanese University（黎巴嫩大学）
ToolSafe: Enhancing Tool Invocation Safety of LLM-based agents via Proactive Step-level Guardrail and Feedback
National Engineering Research Center for Software Engineering, Peking University（软件工程国家工程研究中心，北京大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
FRAME: Feedback-Refined Agent Methodology for Enhancing Medical Research Insights
South China University of Technology（南方科技大学）；HFIPS, Chinese Academy of Sciences（中国科学院HFIPS）；University of Science and Technology of China（中国科学技术大学）；The Third Affiliated Hospital of SuYat-sen University（孙逸德大学第三附属医院）
Enhancing Input-Label Mapping in In-Context Learning with Contrastive Decoding
Beihang University（北航大学）；The University of Sydney（悉尼大学）；University of Liverpool（利物浦大学）；The Hong Kong Polytechnic University（香港理工大学）；Nanyang Technological University（南洋理工大学）
UM6P-CS at SemEval-2022 Task 11: Enhancing Multilingual and Code-Mixed Complex Named Entity Recognition via Pseudo Labels using Multilingual Transformer
Mohammed VI Polytechnic University（穆罕默德六世理工大学）；Sidi Mohamed Ben Abdellah University（西迪·穆罕默德·本·阿卜杜拉大学）；University of Sherbrooke（舍布鲁克大学）
Enhancing the Traditional Chinese Medicine Capabilities of Large Language Model through Reinforcement Learning from AI Feedback
School of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）；School of Computing Technologies, RMIT University（RMIT大学计算技术学院）；Yongdingmenwai Community Health Service Center（永定门外社区卫生服务中心）
CausalAbstain: Enhancing Multilingual LLMs with Causal Reasoning for Trustworthy Abstention
Department of Computer Science, Hong Kong Baptist University（香港 Baptist 大学计算机科学系）；School of Mathematics and Statistics, The University of Melbourne（墨尔本大学数学与统计学学院）；School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）
