Taxonomies represent an arborescence hierarchical structure that establishes relationships among entities to convey knowledge within a specific domain. Each edge in the taxonomy signifies a hypernym-hyponym relationship. Taxonomies find utility in various real-world applications, such as e-commerce search engines and recommendation systems. Consequently, there arises a necessity to enhance these taxonomies over time. However, manually curating taxonomies with neoteric data presents challenges due to limitations in available human resources and the exponential growth of data. Therefore, it becomes imperative to develop automatic taxonomy expansion methods. Traditional supervised taxonomy expansion approaches encounter difficulties stemming from limited resources, primarily due to the small size of existing taxonomies. This scarcity of training data often leads to overfitting. In this paper, we propose FLAME, a novel approach for taxonomy expansion in low-resource environments by harnessing the capabilities of large language models that are trained on extensive real-world knowledge. LLMs help compensate for the scarcity of domain-specific knowledge. Specifically, FLAME leverages prompting in few-shot settings to extract the inherent knowledge within the LLMs, ascertaining the hypernym entities within the taxonomy. Furthermore, it employs reinforcement learning to fine-tune the large language models, resulting in more accurate predictions. Experiments on three real-world benchmark datasets demonstrate the effectiveness of FLAME in real-world scenarios, achieving a remarkable improvement of 18.5% in accuracy and 12.3% in Wu & Palmer metric over eight baselines. Furthermore, we elucidate the strengths and weaknesses of FLAME through an extensive case study, error analysis and ablation studies on the benchmarks.
STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths
Georgia Institute of Technology（佐治亚理工学院）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Electronic Science and Technology of China（电子科技大学）
MOSAIC: A Multilingual, Taxonomy-Agnostic, and Computationally Efficient Approach for Radiological Report Classification
Department of Computer Science, University of Copenhagen（哥本哈根大学计算机科学系）；Neurobiology Research Unit, Copenhagen University Hospital（哥本哈根大学医院神经生物学研究单位）；Unumed Aps（Unumed公司）；Department of Diagnostic Radiology, Copenhagen University Hospital（哥本哈根大学医院诊断放射学系）；Department of Clinical Medicine, University of Copenhagen（哥本哈根大学临床医学系）；Cerebriu A/S（Cerebriu公司）；Institute for Human Genetics, Medical University of Innsbruck（因斯布鲁克医学大学人类遗传学研究所）
Self-supervised Topic Taxonomy Discovery in the Box Embedding Space
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Data Science, Lingnan University（岭南大学数据科学学院）；School of Science and Technology, Hong Kong Metropolitan University（香港都会大学科技学院）；Department of Computing, the Hong Kong Polytechnic University（香港理工大学计算学系）
LongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesis
University of Chinese Academy of Sciences（中国科学院大学）；The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所认知与决策智能复杂系统重点实验室）
TEAMMix: Taxonomy Enrichment Augmentation and Minority-augmented Mixing Strategy for LLM-enhanced Weak-Supervised Hierarchical Text Classification
School of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；ZJU-UIUC Institute, Zhejiang University（浙江大学ZJU-UIUC研究院）；Shaoxing K3i Technology Co. Ltd（绍兴K3i科技有限公司）；State Key Laboratory of CAD&CG, Zhejiang University（浙江大学CAD&CG国家重点实验室）
SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification
IBM Research（IBM研究）；University of Copenhagen（哥本哈根大学）；University of Cambridge（剑桥大学）；Rochester Institute of Technology（罗切斯特理工学院）；Qatar Computing Research Institute, HBKU（卡塔尔计算研究学院，哈马德·本·哈利法大学）
