1 code implementation • 22 Feb 2024 • Honghao Gui, Hongbin Ye, Lin Yuan, Ningyu Zhang, Mengshu Sun, Lei Liang, Huajun Chen
Large Language Models (LLMs) demonstrate remarkable potential across various domains; however, they exhibit a significant performance gap in Information Extraction (IE).
1 code implementation • 5 Dec 2023 • Hongbin Ye, Honghao Gui, Aijia Zhang, Tong Liu, Wei Hua, Weiqiang Jia
Knowledge graph construction (KGC) is a multifaceted undertaking involving the extraction of entities, relations, and events.
1 code implementation • 13 Sep 2023 • Hongbin Ye, Tong Liu, Aijia Zhang, Wei Hua, Weiqiang Jia
Our contribution are threefold: (1) We provide a detailed and complete taxonomy for hallucinations appearing in text generation tasks; (2) We provide theoretical analyses of hallucinations in LLMs and provide existing detection and improvement methods; (3) We propose several research directions that can be developed in the future.
3 code implementations • 19 May 2023 • Honghao Gui, Shuofei Qiao, Jintian Zhang, Hongbin Ye, Mengshu Sun, Lei Liang, Huajun Chen, Ningyu Zhang
Traditional information extraction (IE) methodologies, constrained by pre-defined classes and static training paradigms, often falter in adaptability, especially in the dynamic world.
1 code implementation • 15 May 2023 • Hongbin Ye, Honghao Gui, Xin Xu, Xi Chen, Huajun Chen, Ningyu Zhang
This necessitates a system that can handle evolving schema automatically to extract information for KGC.
1 code implementation • 23 Oct 2022 • Hongbin Ye, Ningyu Zhang, Hui Chen, Huajun Chen
Our contributions are threefold: (1) We present a detailed, complete taxonomy for the generative KGC methods; (2) We provide a theoretical and empirical analysis of the generative KGC methods; (3) We propose several research directions that can be developed in the future.
no code implementations • 27 Jan 2022 • Hongbin Ye, Ningyu Zhang, Shumin Deng, Xiang Chen, Hui Chen, Feiyu Xiong, Xi Chen, Huajun Chen
Specifically, we develop the ontology transformation based on the external knowledge graph to address the knowledge missing issue, which fulfills and converts structure knowledge to text.
1 code implementation • 10 Jan 2022 • Ningyu Zhang, Xin Xu, Liankuan Tao, Haiyang Yu, Hongbin Ye, Shuofei Qiao, Xin Xie, Xiang Chen, Zhoubo Li, Lei LI, Xiaozhuan Liang, Yunzhi Yao, Shumin Deng, Peng Wang, Wen Zhang, Zhenru Zhang, Chuanqi Tan, Qiang Chen, Feiyu Xiong, Fei Huang, Guozhou Zheng, Huajun Chen
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.
no code implementations • 2 Dec 2021 • Shumin Deng, Jiacheng Yang, Hongbin Ye, Chuanqi Tan, Mosha Chen, Songfang Huang, Fei Huang, Huajun Chen, Ningyu Zhang
Previous works leverage logical forms to facilitate logical knowledge-conditioned text generation.
no code implementations • 1 Oct 2021 • Hongbin Ye, Ningyu Zhang, Zhen Bi, Shumin Deng, Chuanqi Tan, Hui Chen, Fei Huang, Huajun Chen
Event argument extraction (EAE) is an important task for information extraction to discover specific argument roles.
1 code implementation • 3 Jun 2021 • Ningyu Zhang, Qianghuai Jia, Shumin Deng, Xiang Chen, Hongbin Ye, Hui Chen, Huaixiao Tou, Gang Huang, Zhao Wang, Nengwei Hua, Huajun Chen
Conceptual graphs, which is a particular type of Knowledge Graphs, play an essential role in semantic search.
1 code implementation • 11 Apr 2021 • Xiang Chen, Xin Xie, Zhen Bi, Hongbin Ye, Shumin Deng, Ningyu Zhang, Huajun Chen
Although the self-supervised pre-training of transformer models has resulted in the revolutionizing of natural language processing (NLP) applications and the achievement of state-of-the-art results with regard to various benchmarks, this process is still vulnerable to small and imperceptible permutations originating from legitimate inputs.
1 code implementation • 6 Apr 2021 • Luoqiu Li, Zhen Bi, Hongbin Ye, Shumin Deng, Hui Chen, Huaixiao Tou
In this paper, we propose a novel legal application of legal provision prediction (LPP), which aims to predict the related legal provisions of affairs.
no code implementations • COLING 2020 • Haiyang Yu, Ningyu Zhang, Shumin Deng, Hongbin Ye, Wei zhang, Huajun Chen
Current supervised relational triple extraction approaches require huge amounts of labeled data and thus suffer from poor performance in few-shot settings.
no code implementations • 14 Sep 2020 • Hongbin Ye, Ningyu Zhang, Shumin Deng, Mosha Chen, Chuanqi Tan, Fei Huang, Huajun Chen
In this paper, we revisit the end-to-end triple extraction task for sequence generation.
Ranked #9 on Relation Extraction on WebNLG
1 code implementation • 14 Sep 2020 • Luoqiu Li, Xiang Chen, Hongbin Ye, Zhen Bi, Shumin Deng, Ningyu Zhang, Huajun Chen
Fine-tuning pre-trained models have achieved impressive performance on standard natural language processing benchmarks.