13 citations · 24 across the 7 of their papers we have counts for
7 papers
Light Up the Shadows: Enhance Long-Tailed Entity Grounding with Concept-Guided Vision-Language Models
Yikai Zhang, Qianyu He, Xintao Wang +3
Multi-Modal Knowledge Graphs (MMKGs) have proven valuable for various downstream tasks. However, scaling them up is challenging because building large-scale MMKGs often introduces…
Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understanding
Yanda Li, Dixuan Wang, Jiaqing Liang +4
Large Language Models (LLMs) have demonstrated good performance in many reasoning tasks, but they still struggle with some complicated reasoning tasks including logical reasoning.…
Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases
Wenhao Huang, Qianyu He, Zhixu Li +2
Definition bias is a negative phenomenon that can mislead models. Definition bias in information extraction appears not only across datasets from different domains but also within…
Laying the Foundation First? Investigating the Generalization from Atomic Skills to Complex Reasoning Tasks
Yuncheng Huang, Qianyu He, Yipei Xu +2
Current language models have demonstrated their capability to develop basic reasoning, but struggle in more complicated reasoning tasks that require a combination of atomic skills,…
Enhancing Quantitative Reasoning Skills of Large Language Models through Dimension Perception
Yuncheng Huang, Qianyu He, Jiaqing Liang +3
Quantities are distinct and critical components of texts that characterize the magnitude properties of entities, providing a precise perspective for the understanding of natural la…
KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases
Xintao Wang, Qianwen Yang, Yongting Qiu +5
Large language models (LLMs) have demonstrated impressive impact in the field of natural language processing, but they still struggle with several issues regarding, such as complet…