Contextual Prompt Embeddings for Precise LLM Knowledge Updates
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Solution Overview
Problem
Large language models face challenges in maintaining up-to-date information, ensuring safety, and controlling quality, with current editing techniques being inefficient and costly, leading to significant overhead and precision drops.
Innovation Solution
The use of a contextual prompt generator (CPG) to generate prompt embeddings that represent new or updated information, which are combined with token embeddings to update the LLM's responses, allowing efficient and controlled model editing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current editing techniques are used to update LLM information, then the model can be edited, but the editing process is inefficient and costly with significant overhead and precision drops
Solution Approach 1:
The patent introduces a contextual prompt generator (CPG) as an intermediary component between the input and the LLM. The CPG generates contextual prompts that guide the LLM to produce accurate outputs without requiring direct modification of the LLM's internal parameters. This mediator approach enables efficient editing while maintaining precision, as the CPG can be updated independently and generates context-specific guidance that preserves the LLM's core capabilities while incorporating new information.
Solution Approach 2:
The system segments the LLM functionality into the core language model and the separate contextual prompt generator. This segmentation allows the CPG to be edited and updated independently without affecting the entire LLM, thereby improving editing efficiency. The CPG handles the task of incorporating new information, while the LLM maintains its general language understanding, reducing the overhead and precision loss associated with editing the entire model.
2Manufacturing precision
If LLM parameters are increased to improve output quality, then the model capabilities are boosted, but the computational cost and complexity increase significantly
Solution Approach 1:
The contextual prompt generator serves as an intermediary that enhances output quality without increasing the complexity of the core LLM. By generating context-specific prompts and guidance, the CPG improves the LLM's performance on specific tasks while keeping the base model's parameter count and complexity unchanged. This approach allows for high output quality with manageable system complexity.
Solution Approach 2:
The contextual prompt generator is designed to be a universal component that can enhance the LLM's performance across multiple tasks and domains. Rather than creating specialized high-parameter models for each task, the CPG provides multi-functional support by generating appropriate contextual guidance for various types of inputs, thereby improving output quality without proportionally increasing overall system complexity.
3Adaptability or versatility
If the LLM is updated with new information, then the model reflects current knowledge, but the editing process causes precision drops and requires significant computational overhead
Solution Approach 1:
The contextual prompt generator acts as an intermediary layer that incorporates new information without directly modifying the LLM's trained parameters. The CPG can be updated with fresh information and generates contextual prompts that reflect current knowledge while leveraging the LLM's stable, pre-trained understanding. This separation allows the system to adapt to new information while maintaining the precision of the core language model.
Solution Approach 2:
The system performs preliminary action by pre-computing and storing contextual information in the CPG that can be quickly retrieved and applied during inference. Rather than retraining the LLM with new information, the CPG is updated in advance with relevant contextual data, allowing the LLM to maintain its precision while reflecting updated knowledge through the contextual prompts generated by the CPG.
Data Source
AI summary
A method includes receiving an input for a large language model (LLM) from a user. The method also includes generating one or more token embeddings based on the input. The method further includes generating one or more prompt embeddings based on the input using a contextual prompt generator (CPG), the one or more prompt embeddings representing new or updated information that is not contained in existing knowledge of the LLM. The method also includes providing the one or more token embeddings and the one or more prompt embeddings to the LLM. In addition, the method includes outputting a prediction based on the one or more token embeddings and the one or more prompt embeddings using the LLM, wherein the prediction reflects the new or updated information represented by the one or more prompt embeddings.