5 papers
BCL: Bayesian In-Context Learning Framework for Information Extraction
Haoliang Liu, Chengkun Cai, Xu Zhao +7
Existing information extraction (IE) tasks increasingly adopt in-context learning (ICL) with large language models. However, current approaches either show inconsistent performance…
E2CAR: An Efficient 2D-CNN Framework for Real-Time EEG Artifact Removal on Edge Devices
Haoliang Liu, Chengkun Cai, Xu Zhao +1
Electroencephalography (EEG) signals are frequently contaminated by artifacts, affecting the accuracy of subsequent analysis. Traditional artifact removal methods are often computa…
Bayesian Optimization for Controlled Image Editing via LLMs
Chengkun Cai, Haoliang Liu, Xu Zhao +6
In the rapidly evolving field of image generation, achieving precise control over generated content and maintaining semantic consistency remain significant limitations, particularl…
The Role of Deductive and Inductive Reasoning in Large Language Models
Chengkun Cai, Xu Zhao, Haoliang Liu +5
Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning tasks, yet their reliance on static prompt structures and limited adaptability to complex scenar…
of Thoughts: Temperature Tree Elicits Reasoning in Large Language Models
Chengkun Cai, Xu Zhao, Yucheng Du +2
Large Language Models (LLMs) have emerged as powerful tools in artificial intelligence, especially in complex decision-making scenarios, but their static problem-solving strategies…