LLM Query Relevance Filtering for Enterprise Resource Allocation
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Solution Overview
Problem
Large language models (LLMs) in enterprises face resource inefficiencies due to processing unrelated queries, tying up computational resources needed for more relevant tasks.
Innovation Solution
Implementing a machine learning model to filter queries based on relevance to an enterprise's domain, generating scores to determine query relevance and manage resource allocation, with a secondary model assessing LLM responses for continuous improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs process all incoming queries without filtering, then query response coverage is improved, but computational resource utilization deteriorates due to processing unrelated queries
Solution Approach 1:
The patent applies a machine learning model to pre-filter queries before they reach the LLM, assigning relevance scores in advance. This preliminary action identifies and filters out unrelated queries, ensuring that only relevant queries consume LLM computational resources, thus resolving the contradiction between comprehensive query coverage and resource efficiency
Solution Approach 2:
The patent introduces a machine learning model as an intermediary component between query submission and LLM processing. This intermediary assesses query relevance and manages resource allocation by determining which queries should proceed to the LLM, thereby preventing resource waste on unrelated queries while maintaining appropriate query coverage
2Productivity
If a machine learning model filters queries before LLM processing, then computational resource efficiency is improved, but query processing complexity increases
Solution Approach 1:
The patent segments the query processing workflow into distinct stages: an initial machine learning model for relevance assessment, followed by LLM processing only for high-scoring queries. This segmentation separates the filtering function from the generation function, improving computational efficiency while managing complexity through modular architecture
Solution Approach 2:
The patent changes the parameter of query processing by introducing a relevance score threshold. Queries are transformed from a binary processed/not-processed state to a scored state, allowing systematic filtering based on score thresholds. This parameter change enables efficient resource allocation while maintaining processing flexibility
3Productivity
If LLMs focus only on enterprise-specific queries through filtering, then resource allocation is improved, but measurement precision of query relevance must increase to avoid false filtering
Solution Approach 1:
The patent implements feedback mechanisms where LLM responses and user interactions are used to continuously retrain and refine the machine learning model. This feedback loop improves the precision of relevance assessment over time, ensuring that filtering decisions become more accurate while maintaining optimal resource allocation for enterprise-specific queries
Data Source
AI summary
Techniques for filtering queries to a large language model (LLM) based on their relevance to an enterprise domain associated with the LLM involve training a machine learning model using historical LLM query data and associated relevance scores. These scores indicate how closely a query relates to the enterprise's operations. The trained model is then applied to new input queries, generating relevance scores for the input queries. Queries meeting a predetermined relevance threshold are passed to the LLM for processing. For queries falling below this threshold, remedial actions are taken instead of processing by the LLM. The techniques optimize computational resource allocation by prioritizing queries relevant to the enterprise while filtering out less pertinent ones. The techniques create a relevance-based gatekeeping mechanism for LLM query processing, enhancing efficiency and focusing the LLM's capabilities on enterprise-specific tasks.