Since the dynamic characteristics of knowledge graphs, many inductive
knowledge graph representation learning (KGRL) works have been proposed in
recent years, focusing on enabling prediction over new entities. NeuralKG-ind
is the first library of inductive KGRL as an important update of NeuralKG
library. It includes standardized processes, rich existing methods, decoupled
modules, and comprehensive evaluation metrics. With NeuralKG-ind, it is easy
for researchers and engineers to reproduce, redevelop, and compare inductive
KGRL methods. The library, experimental methodologies, and model
re-implementing results of NeuralKG-ind are all publicly released at
this https URL .

该研究论文介绍了针对新实体预测的归纳知识图谱表示学习 (Inductive KGRL) 方法，在此基础上推出更新版的 NeuralKG-ind 库，其中包括标准化处理、丰富的现有方法、分离模块和全面的评估指标，使研究人员和工程师能够轻松地再现、重新开发和比较归纳 KGRL 方法。

NeuralKG-ind：Python 库，用于归纳知识图表示学习

NeuralKG-ind: A Python Library for Inductive Knowledge Graph  Representation Learning

The emergence of large language models (LLMs) has resulted in the production
of LLM-generated texts that is highly sophisticated and almost
indistinguishable from texts written by humans. However, this has also sparked
concerns about the potential misuse of such texts, such as spreading
misinformation and causing disruptions in the education system. Although many
detection approaches have been proposed, a comprehensive understanding of the
achievements and challenges is still lacking. This survey aims to provide an
overview of existing LLM-generated text detection techniques and enhance the
control and regulation of language generation models. Furthermore, we emphasize
crucial considerations for future research, including the development of
comprehensive evaluation metrics and the threat posed by open-source LLMs, to
drive progress in the area of LLM-generated text detection.

该研究对大型语言模型生成文本的检测技术现状及未来方向进行了综述，并提出了发展全面的评估指标和威胁控制方案等必要措施。关注点包括大型语言模型的开源威胁和其所产生的可能的误传信息问题。