Context Profile Selection for Low-Latency Foundation Model Queries
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Solution Overview
Problem
Foundation models face limitations in providing specialized domain knowledge and require significant computing resources, leading to inefficiencies in computing environments with limited processing capacity and strict latency requirements.
Innovation Solution
A context generation system that iteratively or simultaneously analyzes multiple context profiles to determine the most relevant response within a latency budget, using a foundation model to generate context profiles with varying complexity and associated costs, and selects the best response based on relevancy scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If foundation models are used to provide specialized domain knowledge, then response accuracy is improved, but computing resource consumption increases
Solution Approach 1:
The system segments the context generation process into multiple context profiles with varying levels of detail and complexity. Each profile represents a different granularity of information retrieval, allowing the system to select appropriate segments based on query requirements rather than always generating the most comprehensive context, thus reducing unnecessary computing resource consumption while maintaining accuracy when needed.
Solution Approach 2:
The system changes parameters of context generation by creating context profiles with different complexity levels, retrieval depths, and detail parameters. By adjusting these parameters based on query characteristics and latency budgets, the system can optimize the balance between response accuracy and computing resource consumption, using more resources only when higher accuracy is required.
2Measurement precision
If comprehensive context is generated for foundation models, then response relevance is improved, but processing latency increases
Solution Approach 1:
The system dynamically adjusts the level of context generation by selecting from multiple context profiles based on query characteristics and latency constraints. Rather than using a static comprehensive context generation approach, the system adapts the context retrieval depth and complexity in real-time, generating more detailed context only when the query requires it and the latency budget permits, thus balancing relevance and latency.
Solution Approach 2:
The system generates partial context information by selecting context profiles that provide sufficient but not necessarily complete information for answering queries. By using context profiles with varying levels of completeness, the system avoids the excessive action of always generating comprehensive context, reducing processing latency while maintaining adequate response relevance through selective context retrieval.
3Measurement precision
If multiple context profiles are analyzed to improve response quality, then response accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the context analysis process into distinct context profiles that can be independently evaluated. Each profile represents a different approach or level of context retrieval, allowing the system to analyze multiple profiles without creating a monolithic complex system. The segmentation enables modular processing where each profile can be generated and evaluated separately, reducing overall system complexity while maintaining the ability to compare and select the best response.
Data Source
AI summary
A context analysis system receives a query from a user. The context analysis system generates one or multiple context profiles and generates a prompt for a foundation model for each of the context profiles. The context analysis system analyzes each of the context profiles and generates a relevancy score. The context analysis system selects one of the context profiles based on the relevancy score. In some examples, the context analysis system iteratively determines predicted latencies and relevancies of processing a query in conjunction with a generated context and, based on the predicted latencies and/or relevancies, processes the query using a foundation model, such as a large language model (LLM).