Neural NLP Training with Pretrained LLM Document Embeddings

The patent extracts and separates the knowledge representation component from the full LLM training process. Instead of training the entire LLM for each task, the system extracts knowledge in the form of document embeddings and sentence embeddings from pre-trained LLMs, then uses these extracted representations for task-specific NLP model training, significantly reducing computational requirements while maintaining performance.

The patent introduces document embeddings and sentence embeddings as intermediary representations between the pre-trained LLM and the task-specific NLP model. These embeddings serve as a bridge that transfers knowledge from the large language model without requiring full LLM training, enabling efficient task adaptation through intermediate knowledge representation layers.

The patent applies local quality by implementing factuality constraints at the sentence level rather than requiring comprehensive knowledge input across all domains. The system uses sentence embeddings to enforce factuality constraints locally on individual sentences during training, allowing faster training with limited knowledge input while maintaining accuracy through targeted sentence-level validation.

The patent replaces the mechanical system of extensive knowledge input with a more efficient mechanism using sentence embeddings and factuality constraints. Instead of relying on large volumes of knowledge data, the system substitutes this with structured sentence-level representations that encode factual information, enabling both faster training and maintained accuracy.