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A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation
This research introduces a novel text generation model that combines BERT's semantic interpretation strengths with GPT-4's generative capabilities, establishing a high standard in generating coherent, contextually accurate language.
Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation
This study proposes a scheme to process graph structure data by combining graph neural network (GNN), so that the model can capture the complex relationship between entities, thereby improving the knowledge consistency and reasoning ability of the generated text.
TRIZ Method for Urban Building Energy Optimization: GWO-SARIMA-LSTM Forecasting model
The main contribution of this research is the development of a robust model that leverages the strengths of TRIZ and advanced deep learning techniques, improving the accuracy of energy consumption predictions.
Automated Genre-Aware Article Scoring and Feedback Using Large Language Models
This paper focuses on the development of an advanced intelligent article scoring system that not only assesses the overall quality of written work but also offers detailed feature-based scoring tailored to various article genres.
Contrastive Learning for Knowledge-Based Question Generation in Large Language Models
With the rapid development of artificial intelligence technology, especially the increasingly widespread application of question-and-answer systems, high-quality question generation has become a key component in supporting the development of these systems.
