Reduced-Dimensionality Embeddings for Low-Latency Semantic Retrieval
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Solution Overview
Problem
The increasing size of embeddings in machine-learning systems, particularly in large language models (LLMs), hinders computational efficiency and user experience due to elevated computational latency and cost, necessitating a need for effective dimensionality reduction.
Innovation Solution
Implementing a dimensionality reduction model that transforms high-dimensionality embeddings into low-dimensionality embeddings using techniques like pairwise loss, top-k loss, and ranking loss, while preserving semantic and structural relationships, and optimizing parameters through gradient descent algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-dimensionality embeddings are used in LLMs, then representation accuracy and model performance are improved, but computational latency and processing cost increase
Solution Approach 1:
The patent extracts and removes redundant dimensions from high-dimensionality embeddings using dimensionality reduction models (such as Autoencoders, PCA, or t-SNE). The system retains only the essential information by projecting embeddings into lower-dimensional spaces while preserving semantic meaning, thereby reducing computational latency without sacrificing representation accuracy.
Solution Approach 2:
The patent changes the dimensional parameter of embeddings by transforming them from high-dimensional vectors to low-dimensional vectors. This parameter transformation is achieved through learned projection matrices or decomposition models that map the original embedding space to a reduced-dimensional subspace, maintaining semantic integrity while reducing computational burden.
2Measurement precision
If high-dimensionality embeddings are used in LLMs, then representation accuracy and model performance are improved, but processing cost increases
Solution Approach 1:
The system extracts and eliminates unnecessary computational operations by reducing the number of dimensions in embeddings. This decreases the number of floating-point operations required for similarity calculations, attention mechanisms, and other LLM operations, thereby reducing processing cost and energy consumption while preserving semantic representation quality.
Solution Approach 2:
The patent transforms the dimensional parameter of embeddings to reduce computational complexity. By projecting high-dimensional embeddings into lower-dimensional spaces, the system reduces the computational footprint of subsequent processing operations, leading to lower processing costs without compromising the representational capability needed for accurate language understanding and generation.
3Productivity
If embedding dimensionality is reduced, then computational efficiency and cost are improved, but semantic information may be lost
Solution Approach 1:
The patent employs feedback mechanisms in the form of reconstruction loss functions that continuously monitor and adjust the dimensionality reduction process. The system optimizes the projection by minimizing the difference between original high-dimensional embeddings and reconstructed low-dimensional embeddings, ensuring that semantic information is preserved while achieving computational efficiency.
Solution Approach 2:
The patent carefully manages the dimensional parameter transformation by using learned projection matrices that are optimized to preserve semantic structure. The reduction is performed in a way that maintains the most important semantic variations in the data, ensuring that computational efficiency gains do not come at the expense of semantic information loss.
Data Source
AI summary
Methods, systems, devices, and non-transitory computer readable media for generating reduced dimensionality embeddings are provided. The disclosed technology can include receiving high-dimensionality embeddings comprising high-dimensionality vectors comprising a first plurality of dimensions. Based on inputting the high-dimensionality embeddings into a dimensionality reduction model that is configured to reduce the dimensionality of vectors of embeddings, a plurality of low-dimensionality embeddings comprising a plurality of low-dimensionality vectors can be generated. Each of the plurality of low-dimensionality vectors can be based on the high-dimensionality vectors of the high-dimensionality embeddings and can comprise a second plurality of dimensions that is smaller than the first plurality of dimensions of the high-dimensionality vectors. The plurality of low-dimensionality embeddings can be stored.