Semantic Cache Thresholding for Accurate Query Matching
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Solution Overview
Problem
Fuzzy matching in semantic caches for question answering systems using generative AI models leads to incorrect responses and cache misses due to the rigidity and arbitrariness of similarity threshold choices, increasing latency and costs.
Innovation Solution
Generate a distance-dependent F1-optimized similarity threshold based on semantically preserving synthetic variations of queries, using embeddings and training unique thresholds for each query in a semantic cache to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed similarity threshold is used for fuzzy matching in semantic cache, then the system can operate with simple structure, but it causes incorrect responses and cache misses due to rigidity of threshold choice
Solution Approach 1:
The patent implements dynamic similarity thresholds that are computed at query time based on the specific query characteristics and cache contents, rather than using a fixed threshold. This allows the threshold to adapt to different query types and contexts, improving response accuracy while maintaining system reliability.
Solution Approach 2:
The system changes the similarity threshold parameter dynamically based on query-specific factors such as query length, token distribution, and semantic characteristics. This parameter adaptation resolves the contradiction by allowing the threshold to be flexible rather than rigid, improving reliability without requiring complex system restructuring.
2Loss of time
If a lower similarity threshold is used to increase cache hits, then response time is reduced, but incorrect responses increase due to overly permissive matching
Solution Approach 1:
The similarity threshold is dynamically adjusted based on the specific query being processed, allowing the system to use lower thresholds for queries that can tolerate more flexibility while maintaining higher thresholds for queries requiring precision. This dynamic adjustment reduces response time for suitable queries without increasing incorrect responses.
Solution Approach 2:
Different similarity thresholds are applied to different queries based on their individual characteristics such as semantic complexity, token distribution, and expected answer type. This local optimization allows each query to benefit from an appropriately tuned threshold, improving overall response time without sacrificing accuracy.
3Reliability
If a higher similarity threshold is used to ensure accuracy, then incorrect responses are reduced, but cache misses increase leading to increased latency and costs
Solution Approach 1:
The system dynamically computes similarity thresholds that are optimized for each query, allowing higher thresholds when accuracy is critical and lower thresholds when cache hit probability is more important. This dynamic approach reduces cache misses and associated latency/costs while maintaining response accuracy through query-specific threshold selection.
Solution Approach 2:
The similarity threshold parameter is changed and optimized for each query based on factors such as query semantics, expected answer confidence, and cache content characteristics. This parameter optimization ensures that queries requiring high accuracy use appropriate thresholds while avoiding unnecessary cache misses that would increase latency and costs.
4Device complexity
If arbitrary threshold choices are made to simplify implementation, then device complexity is reduced, but manufacturing precision of matching accuracy deteriorates
Solution Approach 1:
The system pre-computes and stores query embeddings and similarity metrics during query processing, making this information available for subsequent threshold computations. This preliminary action simplifies the threshold selection mechanism by providing pre-processed data while improving matching accuracy through informed threshold decisions.
Solution Approach 2:
The system automatically computes and selects appropriate similarity thresholds based on query characteristics and cache contents, eliminating the need for manual or arbitrary threshold configuration. This self-service approach simplifies implementation by removing complex manual tuning while improving matching accuracy through data-driven threshold selection.
Data Source
AI summary
A method, computer program product, and computing system for processing a dataset of query-answer pairs including generating synthetic variations of queries from a dataset of query-answer pairs, generating an embedding dataset by transforming the synthetic variations of queries into synthetic query embeddings and queries in the dataset of query-answer pairs into query embeddings, storing at least a portion of the synthetic query embeddings and query embeddings in a semantic cache, wherein each synthetic query embedding and query embedding stored in the semantic cache is associated with a respective distance threshold determined based at least in part on a measure of semantic similarity between the synthetic query and the query used to generate a particular query embedding, and processing a subsequent query using the synthetic variations of queries from the semantic cache and the distance thresholds.