Language Model Routing for Vehicle Query Processing Constraints
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Solution Overview
Problem
Current large language models require significant computational power, memory, and resources, making them incompatible with automotive computing infrastructure and inefficient for processing complex queries.
Innovation Solution
A method involving a first smaller language model to evaluate queries, select an appropriate second language model based on query complexity, and process queries using that model, potentially with external servers, to reduce computational and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large language models are used to process complex queries, then query processing accuracy is improved, but computational power requirements and processing time increase significantly
Solution Approach 1:
The system segments the language model processing task into multiple smaller models with different capabilities. Instead of using one large model for all queries, the system divides models into tiers (e.g., small, medium, large) and routes queries to appropriate models based on complexity evaluation, thereby reducing overall computational power requirements while maintaining accuracy for complex queries.
Solution Approach 2:
The system dynamically selects which language model to use based on the evaluated complexity of each query. The model selection is not static but adapts to the specific query characteristics, allowing the system to use smaller models for simple queries and larger models only when necessary for complex queries, thus optimizing the balance between accuracy and computational power.
2Measurement precision
If large language models are used to process complex queries, then query processing accuracy is improved, but processing time increases considerably
Solution Approach 1:
The system segments the processing workload by distributing queries across multiple models of different sizes. Simple queries are handled by smaller, faster models while complex queries are routed to larger, more accurate models. This segmentation reduces the average processing time across all queries while maintaining high accuracy for complex cases.
Solution Approach 2:
The system dynamically adjusts model selection based on query complexity evaluation. By assessing each query's complexity before processing, the system can choose the minimum necessary model size, avoiding the use of large models for simple queries and thereby reducing overall processing time while preserving accuracy when needed.
3Adaptability or versatility
If large language models are deployed in vehicles, then language processing capabilities are enhanced, but computational resources and memory requirements exceed automotive infrastructure limitations
Solution Approach 1:
The system segments the language model infrastructure into multiple smaller models that can be distributed across available computational resources. Instead of requiring one large model that exceeds automotive limitations, the system uses several smaller models with different capabilities, allowing the vehicle's limited computational resources and memory to be efficiently utilized while still providing enhanced language processing capabilities.
Solution Approach 2:
The system creates a universal language model selection framework that can handle diverse query types using appropriate models from the plurality. This multi-functional approach allows the automotive system to provide versatile language processing capabilities across different scenarios without requiring excessive computational resources, as each model in the plurality is optimized for specific types of language tasks.
Data Source
AI summary
A computer-implemented method is disclosed. The method including receiving an input query from a user; evaluating the input query based, at least in part, on a first language model; selecting a second language model from a plurality of second language models based, at least in part, on evaluating the input query; processing the input query based, at least in part, on the second language model; and performing an operation based, at least in part, on the processing of the input query.