Efficient Application of Transformer Structure in Text Generation Tasks

This study focuses on exploring the efficient application methods and performance of the Transformer structure in text generation tasks. Based on the core principles of Transformer, an application process including data preprocessing, pre-training model screening, training mechanism improvement, decoding strategy formulation and reasoning efficiency enhancement is built. System experiments are carried out based on the GEM 2024 shared task dataset, and multi-dimensional indicators such as BLEU, ROUGE-L, and Perplexity are used for evaluation. The traditional RNN model is far inferior to Transformer variants such as T5 in text generation accuracy and fluency. The Top-p sampling strategy is the best in balancing accuracy and diversity. The structural ablation experiment further highlights the key position of components such as multi-head attention and position encoding. The Transformer structure shows extremely high adaptability and scalability in text generation, which can provide reliable technical support for natural language processing tasks.