Prompt Tuning with Semantic Search and Meta-Prompt Generation

The patent segments the training process into two distinct phases: (1) pre-training the model on large-scale datasets, and (2) fine-tuning on smaller task-specific datasets. This segmentation allows the computationally intensive pre-training to be performed once, while subsequent fine-tuning requires minimal computational resources, thus resolving the contradiction between achieving high performance and reducing ongoing computational requirements.

The patent applies preliminary action by performing comprehensive pre-training on large pretrained models before deployment. This preliminary training establishes a strong foundation that enables the model to achieve state-of-the-art performance while requiring minimal computational resources during actual use, as the heavy lifting has already been completed in advance.

The patent implements feedback mechanisms during the fine-tuning process where the model's performance on task-specific evaluation data continuously guides the selection and weighting of training examples. This feedback loop ensures that data expansion prioritizes examples that actually improve task performance rather than merely increasing dataset size, thus maintaining high task relevance while expanding the training data.

The patent changes the parameters used for data selection from simple similarity metrics to task-performance-oriented criteria. By adjusting the selection parameters to prioritize examples that demonstrate successful task completion or high relevance scores, the system expands the dataset while maintaining or improving task relevance, resolving the contradiction between quantity and quality.

The patent applies partial action by implementing semantic search selectively rather than for all data processing operations. Semantic search is used specifically for identifying and retrieving relevant prompts from the repository during the fine-tuning phase, while other processing operations maintain standard speed. This selective application preserves overall processing efficiency while significantly improving data selection quality where it matters most.

The patent introduces an intermediary semantic search mechanism that acts as a bridge between the available prompt repository and the fine-tuning process. This intermediary component efficiently retrieves highly relevant prompts without requiring exhaustive processing of all available data, thus improving data selection quality while maintaining acceptable processing speeds through intelligent intermediation.