AI-Driven Text Generation: A Novel GPT-Based Approach for Automated Content Creation
Pushpendra Kumar, S. Manikandan, Ravi Kishore
Abstract
By combining the strengths of BERT (Bidirectional Encoding Representations from Transformers) and GPT (Generative Pre-trained Transformer), this study presents a novel method for automated text synthesis. We combine BERT's bidirectional contextual awareness to improve the coherence & relevance of generated text, while utilizing the pre-trained abilities of GPT for innovative and context-aware content generation. In order to provide a more complex and contextually accurate output, our model uses a two-stage architecture, where GPT starts the content production process and BERT repeatedly refines it. We show through extensive experimentation that our approach performs better than others in a variety of text creation tasks, such as question-answering, creative writing, and summarizing. This hybrid GPT-BERT approach represents a major breakthrough in automated text creation techniques, demonstrating not just exceptional fluency & coherence but also a remarkable capacity to adapt to a variety of linguistic circumstances. The results highlight the possibility of integrating transformer-based models to produce language creation that is more complex and contextually sensitive.