Multi-LLM Response Bridging for Low-Latency Accurate Answers
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Solution Overview
Problem
Large generative models require significant computational resources and latency, making them impractical for many client devices, while smaller models are less accurate and prone to inaccuracy.
Innovation Solution
Utilize a smaller generative model on a client device and a larger model on a remote server, where the smaller model generates an initial response with reduced latency and the larger model generates a refined response to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a larger generative model is used, then content accuracy is improved, but interaction latency increases
Solution Approach 1:
The response generation process is segmented into two stages: first, a smaller model generates an initial response quickly to reduce latency; second, a larger model refines the response to improve accuracy. This segmentation allows the system to benefit from both fast initial response and high accuracy refinement without incurring the full latency cost of using only the larger model.
Solution Approach 2:
The smaller model performs preliminary action by generating an initial response before the larger model processes the refinement. This preliminary response provides a head start, reducing the perceived latency to the user while the larger model works in the background to enhance accuracy.
2Productivity
If a smaller generative model is used, then computational efficiency is improved, but content accuracy deteriorates
Solution Approach 1:
The system segments the computational workload between two models of different sizes. The smaller model handles the initial response generation with high computational efficiency, while the larger model handles the refinement task to ensure content accuracy. This segmentation allows each model to operate in its optimal performance zone.
Solution Approach 2:
The smaller model acts as an intermediary that prepares an initial response which is then passed to the larger model for refinement. This intermediary step allows the system to leverage the computational efficiency of the smaller model while ultimately achieving the accuracy benefits of the larger model.
3Measurement precision
If a larger generative model is deployed on a client device, then response quality is improved, but device resource requirements increase
Solution Approach 1:
The system segments the model deployment strategy by placing a smaller model on the client device for initial response generation and a larger model on a remote server for refinement. This segmentation allows the client device to operate within its resource constraints while still achieving high response quality through the remote larger model.
Solution Approach 2:
The solution moves from a single-dimension approach (one model on one device) to a multi-dimensional approach by distributing different model sizes across different computational environments (client device and remote server). This dimensional change allows the system to overcome the resource limitations of the client device.
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.