Task-Specific Text Generation Model Fine-Tuning

The patent applies preliminary action by pre-training a general text generation model on large-scale corpora before fine-tuning for specific tasks. This pre-trained model serves as a foundation that captures general language patterns and capabilities, allowing subsequent fine-tuning on smaller task-specific datasets to achieve high performance without requiring enormous amounts of task-specific training data.

The patent segments the training process into two distinct stages: pre-training on general large-scale corpora and fine-tuning on task-specific smaller datasets. This segmentation allows the model to first learn general language representations from abundant data, then specialize in specific tasks with limited data, thereby reducing dependency on large task-specific training corpora while maintaining high generation quality.

The pre-trained general model serves as a ready-to-use foundation that encapsulates general language capabilities. By leveraging this pre-computed model, the system avoids the complexity of training models from scratch for each specific task, significantly reducing device complexity and training costs while maintaining high generation quality through fine-tuning.

The patent uses the pre-trained general model as a template or copy that can be adapted to multiple specific tasks through fine-tuning. Instead of creating new models from scratch for each task, the system copies the successful pre-trained architecture and adapts it to specific tasks using smaller datasets, thereby reducing overall system complexity and computational resources required.

The patent implements universality by designing a single pre-trained general text generation model that can be adapted to multiple different task types through fine-tuning. This universal model serves various functions (translation, summarization, question-answering, etc.) by adjusting task indication information and training data, achieving high versatility without requiring separate specialized models for each task.

The model employs dynamics by allowing flexible adjustment of task indication information and training parameters during the fine-tuning process. The same pre-trained model can dynamically adapt to different task requirements by modifying input task indications and training data, enabling versatile performance across multiple tasks while maintaining a relatively simple base model structure.