no code implementations • 6 May 2024 • Qizhou Chen, Taolin Zhang, Xiaofeng He, Dongyang Li, Chengyu Wang, Longtao Huang, Hui Xue
Model editing aims to correct outdated or erroneous knowledge in large language models (LLMs) without the need for costly retraining.
no code implementations • 4 May 2024 • Taolin Zhang, Dongyang Li, Qizhou Chen, Chengyu Wang, Longtao Huang, Hui Xue, Xiaofeng He, Jun Huang
The reordering learning process is divided into two steps according to the quality of the generated responses: document order adjustment and document representation enhancement.
no code implementations • 17 Mar 2024 • Junbing Yan, Chengyu Wang, Taolin Zhang, Xiaofeng He, Jun Huang, Longtao Huang, Hui Xue, Wei zhang
KEPLMs are pre-trained models that utilize external knowledge to enhance language understanding.
1 code implementation • 13 Dec 2023 • Qian Chen, Taolin Zhang, Dongyang Li, Xiaofeng He
The minimal feature removal problem in the post-hoc explanation area aims to identify the minimal feature set (MFS).
no code implementations • 12 Nov 2023 • Ruyao Xu, Taolin Zhang, Chengyu Wang, Zhongjie Duan, Cen Chen, Minghui Qiu, Dawei Cheng, Xiaofeng He, Weining Qian
In the experiments, we evaluate KANGAROO over various knowledge-aware and general NLP tasks in both full and few-shot learning settings, outperforming various KEPLM training paradigms performance in closed-domains significantly.
no code implementations • 12 Nov 2023 • Junbing Yan, Chengyu Wang, Taolin Zhang, Xiaofeng He, Jun Huang, Wei zhang
Reasoning is a distinctive human capacity, enabling us to address complex problems by breaking them down into a series of manageable cognitive steps.
no code implementations • 18 May 2023 • Taolin Zhang, Sunan He, Dai Tao, Bin Chen, Zhi Wang, Shu-Tao Xia
In recent years, vision language pre-training frameworks have made significant progress in natural language processing and computer vision, achieving remarkable performance improvement on various downstream tasks.
1 code implementation • 11 Oct 2022 • Taolin Zhang, Junwei DOng, Jianing Wang, Chengyu Wang, Ang Wang, Yinghui Liu, Jun Huang, Yong Li, Xiaofeng He
Recently, knowledge-enhanced pre-trained language models (KEPLMs) improve context-aware representations via learning from structured relations in knowledge graphs, and/or linguistic knowledge from syntactic or dependency analysis.
1 code implementation • 29 Aug 2022 • Taolin Zhang, Chuan Chen, Yaomin Chang, Lin Shu, Zibin Zheng
As special information carriers containing both structure and feature information, graphs are widely used in graph mining, e. g., Graph Neural Networks (GNNs).
1 code implementation • 30 Apr 2022 • Chengyu Wang, Minghui Qiu, Chen Shi, Taolin Zhang, Tingting Liu, Lei LI, Jianing Wang, Ming Wang, Jun Huang, Wei Lin
The success of Pre-Trained Models (PTMs) has reshaped the development of Natural Language Processing (NLP).
1 code implementation • Findings (ACL) 2022 • Dongyang Li, Taolin Zhang, Nan Hu, Chengyu Wang, Xiaofeng He
In this paper, we propose a hierarchical contrastive learning Framework for Distantly Supervised relation extraction (HiCLRE) to reduce noisy sentences, which integrate the global structural information and local fine-grained interaction.
1 code implementation • 2 Dec 2021 • Taolin Zhang, Chengyu Wang, Nan Hu, Minghui Qiu, Chengguang Tang, Xiaofeng He, Jun Huang
Knowledge-Enhanced Pre-trained Language Models (KEPLMs) are pre-trained models with relation triples injecting from knowledge graphs to improve language understanding abilities.
2 code implementations • ACL 2021 • Taolin Zhang, Zerui Cai, Chengyu Wang, Minghui Qiu, Bite Yang, Xiaofeng He
Recently, the performance of Pre-trained Language Models (PLMs) has been significantly improved by injecting knowledge facts to enhance their abilities of language understanding.
1 code implementation • Findings (ACL) 2021 • Taolin Zhang, Chengyu Wang, Minghui Qiu, Bite Yang, Xiaofeng He, Jun Huang
In this paper, we introduce a multi-target MRC task for the medical domain, whose goal is to predict answers to medical questions and the corresponding support sentences from medical information sources simultaneously, in order to ensure the high reliability of medical knowledge serving.