Data mining is used in healthcare organizations for surveillance, diagnosis, and prognosis. According to Health Catalyst, deep learning (DL) is the game-changing technology that will revolutionize healthcare. Deep lea...
详细信息
A sentiment analysis scheme for image and text comments based on multimodal deep learning and spatiotemporal attention is proposed to address the issues of incomplete spatiotemporal considerations, incomplete implemen...
详细信息
Large Language Models (LLMs) have shown remarkable performance on a variety of natural language tasks, but eliciting their abilities for more consistent predictions is still highly reliant on researchers' well-cra...
详细信息
ISBN:
(纸本)9789819794331;9789819794348
Large Language Models (LLMs) have shown remarkable performance on a variety of natural language tasks, but eliciting their abilities for more consistent predictions is still highly reliant on researchers' well-crafted prompts, which often require costly computational consumption as well as onerous trial-and-error efforts. For generative tasks such as open-domain question answering (QA), LLMs face a particularly great challenge. To alleviate the issue in QA tasks, we propose a scheme that can both automatically optimize and efficiently leverage candidate prompts to obtain consistent and accurate predicted answers. Superior self-consistency of LLMs' predictions requires diverse high-quality reasoning paths, thus multiple candidate prompts in the iterative optimization process of automatic prompt engineering (APE) are fully leveraged to drive LLMs to generate multiple reasoning paths leading to improved self-consistency. The evaluation performance of the candidate prompt on the sampled dataset is used as a reference (similar to the weights of the base learners in ensemble learning) to determine its contribution to the final answer. Experimental results demonstrate that our scheme yields more reliable and diverse predicted answers and outperforms conventional self-consistency baseline models in several typical QA benchmark tests.
Detecting and classifying entangled animals is a complex task that requires robust feature extraction and discrimination due to the high similarity in visual features. This study evaluates three contrastive learning a...
详细信息
Massive Open Online Courses (MOOCs) represent an accessible and user-friendly tool for disseminating innovative and cutting-edge topics to broad segments of civil society via online learning platforms, enabling users ...
详细信息
Personalized teaching is a key focus in modern education, aiming to meet individual student needs and improve learning efficiency. Traditional teaching methods struggle to address student differences in large-scale se...
详细信息
The research presents a new efficient machine learning method to classify brain tumors because this task remains vital in fighting the high incidence of brain cancers. The proposed approach unites all its operations i...
详细信息
The primary focus of the current study was to evaluate the role of 5G networks in enhancing real-time communication and interactive learning for English Language teaching (ELT) in the institutions of higher education ...
详细信息
With the development of Vehicular Edge computing (VEC) computing architectures, in the study of task offloading problem, based on the differences in task delay sensitivity and the dynamic characteristics of environmen...
详细信息
With the rapid development of artificial intelligence technology, its application in the field of e-commerce education is becoming increasingly widespread, especially in teaching methods, learning assessment, and the ...
详细信息
暂无评论