Auto-Encoder Bottleneck for Lower-Dimensional Embedding Retrieval
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Solution Overview
Problem
Large language models (LLMs) used in the oil and gas industry face increased computational resource requirements due to high-dimensional embeddings of diverse document corpora, compromising search and retrieval accuracy.
Innovation Solution
A system that employs an auto-encoder with a bottleneck encoder layer to reduce embedding dimensions from high-dimensional to low-dimensional representations while preserving semantic information, using an unsupervised machine learning approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If high-dimensional embeddings are used to represent diverse document corpora, then search and retrieval accuracy is maintained, but computational resource requirements increase
Solution Approach 1:
The embedding model is segmented into two distinct components: a high-dimensional embedding model that preserves semantic accuracy, and a dimensionality reduction model that compresses the embedding space. This segmentation allows each component to specialize - one for accuracy and one for efficiency - resolving the contradiction between maintaining search accuracy and reducing computational resources.
Solution Approach 2:
The patent applies dimensionality reduction by transforming embeddings from a high-dimensional space to a lower-dimensional space through a trained reduction model. This dimensional transformation maintains the essential semantic relationships while significantly reducing the computational burden for storage and processing, directly addressing the resource requirement issue.
2Loss of information
If high-dimensional embeddings are used for diverse document corpora, then semantic information is preserved, but storage and management complexity increases
Solution Approach 1:
The storage system is segmented into two layers: a high-dimensional embedding storage that preserves complete semantic information, and a low-dimensional embedding storage that provides efficient access. The dimensionality reduction model bridges these layers, enabling the system to maintain full semantic information while reducing the complexity of actual storage and management operations.
Solution Approach 2:
The patent creates a compressed copy of the high-dimensional embeddings through the dimensionality reduction model. This low-dimensional copy serves as an efficient representation that maintains essential semantic information while significantly reducing storage requirements and management complexity, allowing the system to handle diverse document corpora more effectively.
3Use of energy by moving object
If dimensionality reduction is applied to embeddings, then computational resources are reduced, but search and retrieval accuracy may be compromised
Solution Approach 1:
The dimensionality reduction model is trained in advance using a training dataset with known ground truth relationships. This preliminary training ensures that the reduced-dimensional embeddings learn to preserve the most important semantic information for accurate search and retrieval. The pre-trained model then applies this learned compression without sacrificing accuracy in production use.
Solution Approach 2:
The system incorporates a feedback mechanism where the dimensionality reduction model is trained and evaluated using retrieval accuracy metrics. The training process adjusts the reduction model's parameters to maximize accuracy while minimizing dimensionality. This feedback loop ensures that the reduced embeddings maintain search and retrieval effectiveness despite the lower dimensionality.
Data Source
AI summary
A large language model (LLM) generates a synthetic query. A first-dimensional embedding model generates a query embedding of the synthetic query. A training dataset of first-dimensional embeddings, within a similarity threshold of the query embedding is retrieved. An auto-encoder is trained with the training dataset. The auto-encoder includes an input layer, a bottleneck encoder layer, and a decoder layer. A second-dimensional embedding model, including the first-dimensional embedding model, and the bottleneck encoder layer of the auto-encoder, is configured. An output of the first-dimensional embedding model is connected to an input of the bottleneck encoder layer, to obtain the second-dimensional embedding model. The second-dimensional embedding model is used to generate second-dimensional embeddings having a second dimension. The second dimension is lower than the first dimension.