13 citations · 41 across the 21 of their papers we have counts for
21 papers
Can Pre-trained Language Models Understand Chinese Humor?
Yuyan Chen, Zhixu Li, Jiaqing Liang +3
Humor understanding is an important and challenging research in natural language processing. As the popularity of pre-trained language models (PLMs), some recent work makes prelimi…
Chain-of-Knowledge: Integrating Knowledge Reasoning into Large Language Models by Learning from Knowledge Graphs
Yifei Zhang, Xintao Wang, Jiaqing Liang +3
Large Language Models (LLMs) have exhibited impressive proficiency in various natural language processing (NLP) tasks, which involve increasingly complex reasoning. Knowledge reaso…
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…
Teaching Large Language Models to Express Knowledge Boundary from Their Own Signals
Lida Chen, Zujie Liang, Xintao Wang +7
Large language models (LLMs) have achieved great success, but their occasional content fabrication, or hallucination, limits their practical application. Hallucination arises becau…
Enhancing Confidence Expression in Large Language Models Through Learning from Past Experience
Haixia Han, Tingyun Li, Shisong Chen +5
Large Language Models (LLMs) have exhibited remarkable performance across various downstream tasks, but they may generate inaccurate or false information with a confident tone. One…
Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction
Zepeng Ding, Wenhao Huang, Jiaqing Liang +2
Relation triple extraction, which outputs a set of triples from long sentences, plays a vital role in knowledge acquisition. Large language models can accurately extract triples fr…