1 code implementation • 21 Jan 2025 • Qinggang Zhang, Shengyuan Chen, Yuanchen Bei, Zheng Yuan, Huachi Zhou, Zijin Hong, Junnan Dong, Hao Chen, Yi Chang, Xiao Huang
Large language models (LLMs) have demonstrated remarkable capabilities in a wide range of tasks, yet their application to specialized domains remains challenging due to the need for deep expertise.
no code implementations • 20 Jan 2025 • Zijin Hong, Hao Wu, Su Dong, Junnan Dong, Yilin Xiao, Yujing Zhang, Zhu Wang, Feiran Huang, Linyi Li, Hongxia Yang, Xiao Huang
LLMs must fully understand the problem-solving process for the original problem to correctly answer RV questions with various combinations of variable values.
no code implementations • 23 Oct 2024 • Junnan Dong, Zijin Hong, Yuanchen Bei, Feiran Huang, Xinrun Wang, Xiao Huang
While emerging benchmarks have been proposed to evaluate LLMs in various domains such as mathematics and computer science, they merely measure the accuracy in terms of the final prediction on multi-choice questions.
no code implementations • 5 Oct 2024 • Shengyuan Chen, Qinggang Zhang, Junnan Dong, Wen Hua, Jiannong Cao, Xiao Huang
Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs.
no code implementations • 8 Jul 2024 • Zhu Wang, Shuang Zhou, Junnan Dong, Chang Yang, Xiao Huang, Shengjie Zhao
Specifically, we aim to maximize the performance improvement (AUC) of a base detector by cutting noisy edges approximated through the nodes with high-confidence labels.
no code implementations • 12 Jun 2024 • Zijin Hong, Zheng Yuan, Qinggang Zhang, Hao Chen, Junnan Dong, Feiran Huang, Xiao Huang
Generating accurate SQL from users' natural language questions (text-to-SQL) remains a long-standing challenge due to the complexities involved in user question understanding, database schema comprehension, and SQL generation.
no code implementations • 3 Jun 2024 • Qinggang Zhang, Keyu Duan, Junnan Dong, Pai Zheng, Xiao Huang
It aims to mine latent relation patterns for inductive KG completion.
no code implementations • 27 May 2024 • Shengyuan Chen, Qinggang Zhang, Junnan Dong, Wen Hua, Qing Li, Xiao Huang
Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs.
no code implementations • 27 May 2024 • Junnan Dong, Qinggang Zhang, Chuang Zhou, Hao Chen, Daochen Zha, Xiao Huang
We are motivated to combine LLMs and prior small models on knowledge graphs (KGMs) for both inferential accuracy and cost saving.
no code implementations • 20 Feb 2024 • Junnan Dong, Qinggang Zhang, Huachi Zhou, Daochen Zha, Pai Zheng, Xiao Huang
Specifically, (i) we propose a two-stage prompting strategy with LLMs to densely embody the image into a scene graph with detailed visual features; (ii) We construct a coupled concept graph by linking the mentioned entities with external facts.
no code implementations • 19 Feb 2024 • Qinggang Zhang, Hao Chen, Junnan Dong, Shengyuan Chen, Feiran Huang, Xiao Huang
Recent advancements in large language models (LLMs) have shown promise in bridging the gap between natural language queries and database management systems, enabling users to interact with databases without the background of SQL.
no code implementations • 11 Dec 2023 • Qinggang Zhang, Junnan Dong, Hao Chen, Daochen Zha, Zailiang Yu, Xiao Huang
However, most state-of-the-art LLMs are closed-source, making it challenging to develop a prompting framework that can efficiently and effectively integrate KGs into LLMs with hard prompts only.
1 code implementation • 18 Nov 2022 • Qinggang Zhang, Junnan Dong, Keyu Duan, Xiao Huang, Yezi Liu, Linchuan Xu
To this end, we propose a novel framework - ContrAstive knowledge Graph Error Detection (CAGED).