Zhoubo Li


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LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings
Xin Xie | Zhoubo Li | Xiaohan Wang | ZeKun Xi | Ningyu Zhang
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: System Demonstrations

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Editing Large Language Models: Problems, Methods, and Opportunities
Yunzhi Yao | Peng Wang | Bozhong Tian | Siyuan Cheng | Zhoubo Li | Shumin Deng | Huajun Chen | Ningyu Zhang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Despite the ability to train capable LLMs, the methodology for maintaining their relevancy and rectifying errors remains elusive. To this end, the past few years have witnessed a surge in techniques for editing LLMs, the objective of which is to alter the behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs. This paper embarks on a deep exploration of the problems, methods, and opportunities related to model editing for LLMs. In particular, we provide an exhaustive overview of the task definition and challenges associated with model editing, along with an in-depth empirical analysis of the most progressive methods currently at our disposal. We also build a new benchmark dataset to facilitate a more robust evaluation and pinpoint enduring issues intrinsic to existing techniques. Our objective is to provide valuable insights into the effectiveness and feasibility of each editing technique, thereby assisting the community in making informed decisions on the selection of the most appropriate method for a specific task or context.


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DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
Ningyu Zhang | Xin Xu | Liankuan Tao | Haiyang Yu | Hongbin Ye | Shuofei Qiao | Xin Xie | Xiang Chen | Zhoubo Li | Lei Li
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base population. DeepKE implements various information extraction tasks, including named entity recognition, relation extraction and attribute extraction. With a unified framework, DeepKE allows developers and researchers to customize datasets and models to extract information from unstructured data according to their requirements. Specifically, DeepKE not only provides various functional modules and model implementation for different tasks and scenarios but also organizes all components by consistent frameworks to maintain sufficient modularity and extensibility. We release the source code at GitHub in https://github.com/zjunlp/DeepKE with Google Colab tutorials and comprehensive documents for beginners. Besides, we present an online system in http://deepke.openkg.cn/EN/re_doc_show.html for real-time extraction of various tasks, and a demo video.