Bridged LLM Response Pipeline for Low-Latency Accurate Output
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Solution Overview
Problem
Large generative models require significant computational resources and latency, making them unsuitable for on-device use and leading to inaccurate outputs from smaller counterparts.
Innovation Solution
Utilize a smaller generative model on a client device and a larger model on a remote server, with the smaller model generating an initial response and the larger model refining it to ensure accuracy and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large generative model is used, then accuracy and robustness improve, but computational resource requirements and latency increase significantly
Solution Approach 1:
The system segments the generative model into two distinct components: a smaller first generative model for initial response generation and a larger second generative model for refinement. This segmentation allows the smaller model to handle latency-sensitive operations while the larger model focuses on accuracy-critical refinement, resolving the contradiction between speed and accuracy.
Solution Approach 2:
The smaller first generative model performs preliminary action by generating an initial response before the larger second generative model refines it. This preliminary generation provides immediate feedback to reduce perceived latency, while the subsequent refinement ensures accuracy without blocking the initial response delivery.
2Reliability
If a large generative model is used, then accuracy improves, but device complexity and memory requirements increase
Solution Approach 1:
The system divides the computational burden by segmenting the model architecture into two parts with different size requirements. The smaller first model can be deployed on resource-constrained devices, while the larger second model resides on more powerful infrastructure, allowing accuracy improvement without uniformly increasing device complexity everywhere.
Solution Approach 2:
The smaller first generative model acts as an intermediary between the user and the larger second generative model. It translates user inputs into refined prompts for the larger model and processes its outputs into final responses, enabling the system to leverage the larger model's accuracy without requiring every device to host it.
3Productivity
If a smaller generative model is used, then computational efficiency improves, but accuracy and robustness deteriorate
Solution Approach 1:
The system merges the strengths of two different model sizes by combining their outputs. The smaller model provides computational efficiency for rapid response generation, while the larger model contributes accuracy through refinement. Their combined effect achieves both high efficiency and high accuracy, resolving the contradiction between productivity and reliability.
Solution Approach 2:
The larger second generative model provides feedback on the initial response generated by the smaller first model. This feedback loop allows the smaller model to operate efficiently while being corrected and enhanced by the larger model's more accurate assessments, thereby maintaining high accuracy without sacrificing the smaller model's computational efficiency.
Data Source
AI summary
Implementations utilize a smaller LLM to generate content responsive to a user query and cause a portion of the generated content to be rendered as an immediate response to the user query. Implementations further utilize a larger LLM to generate content that starts with the portion of the generated content and that includes a refined portion succeeding the portion of the generated content. The refined portion can be rendered succeeding the portion of the generated content. In some implementations, instead of using the smaller LLM, alternatively, the portion of the generated content rendered as the immediate response can be generated based on a default text string or a template, where the template can be determined/selected from a plurality of predefined templates based on a natural language understanding of the user query.