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. Through the combined architecture, the model enhances semantic depth and maintains smooth, human-like text flow, overcoming limitations seen in prior models. Experimental benchmarks reveal that BERT-GPT-4 surpasses traditional models, including GPT-3, T5, BART, Transformer-XL, and CTRL, in key metrics like Perplexity and BLEU, showcasing its superior natural language generation performance. By fully utilizing contextual information, this hybrid model generates text that is not only logically coherent but also aligns closely with human language patterns, providing an advanced solution for text generation tasks. This research highlights the potential of integrating semantic understanding with advanced generative models, contributing new insights for NLP, and setting a foundation for broader applications of large-scale generative architectures in areas such as automated writing, question-answer systems, and adaptive conversational agents.

翻译：本研究提出了一种新颖的文本生成模型，该模型融合了BERT的语义解析优势与GPT-4的生成能力，为生成连贯且语境准确的语言设立了新标准。通过这种融合架构，该模型增强了语义深度并保持了流畅类人的文本流，克服了先前模型存在的局限性。实验基准测试表明，BERT-GPT-4在困惑度（Perplexity）和BLEU等关键指标上超越了包括GPT-3、T5、BART、Transformer-XL和CTRL在内的传统模型，展现了其卓越的自然语言生成性能。通过充分利用上下文信息，该混合模型生成的文本不仅逻辑连贯，而且紧密贴合人类语言模式，为文本生成任务提供了先进的解决方案。本研究凸显了将语义理解与先进生成模型相结合的潜力，为自然语言处理领域贡献了新见解，并为大规模生成架构在自动写作、问答系统和自适应对话代理等领域的更广泛应用奠定了基础。