LLM Embedding Clustering for Categorical Data Accuracy
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Solution Overview
Problem
Existing ML algorithms struggle to effectively understand and cluster categorical data, leading to inefficiencies in fraud and money laundering detection, as they often perform poorly with unlabeled and non-conversational data, and deep learning may overfit when applied to smaller datasets.
Innovation Solution
Integrate large language models (LLMs) into the training process to generate embeddings from categorical data, converting tabular data into narratives and using dimensionality reduction techniques like PCA to enhance ML clustering algorithms, enabling more accurate and comprehensive fraud detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ML algorithms are used to process categorical data, then the system is simple and easy to implement, but the clustering accuracy and understanding of categorical data deteriorates
Solution Approach 1:
The patent introduces LLMs as an intermediary component between traditional ML algorithms and categorical data. The LLM converts categorical data into narrative text representations, which are then processed by dimensionality reduction techniques and fed into ML clustering algorithms. This intermediary transformation enables accurate processing of categorical data while maintaining the simplicity of traditional ML pipelines.
Solution Approach 2:
The patent transforms categorical data from its original discrete state into continuous narrative representations through LLM processing. This parameter change from discrete categories to continuous text embeddings enables the application of continuous-space ML techniques to categorical data, improving clustering accuracy without requiring fundamentally new algorithms.
2Measurement precision
If deep learning is applied to smaller datasets, then the model can capture complex patterns, but the model overfits the data
Solution Approach 1:
The patent applies dimensionality reduction techniques as a preliminary action before feeding data into ML models. By reducing the dimensionality of LLM-generated embeddings beforehand, the system prepares optimized input data that contains essential patterns while removing redundant information, enabling effective learning from smaller datasets without overfitting.
Solution Approach 2:
The patent extracts essential features from high-dimensional LLM embeddings through dimensionality reduction techniques. This extraction process isolates the most important patterns and relationships in the data, removing noise and redundant dimensions that would otherwise cause overfitting in deep learning models trained on small datasets.
3Measurement precision
If LLMs are used to process tabular data, then the understanding of categorical data improves, but data privacy risks increase due to potential external sharing
Solution Approach 1:
The patent introduces dimensionality reduction techniques as an intermediary processing step between LLMs and external systems. This intermediary transformation converts detailed categorical embeddings into compressed representations that retain essential patterns for fraud detection while removing sensitive information, enabling safe external sharing without compromising categorical data understanding.
Data Source
AI summary
An autonomous machine learning (ML) system and methods are provided that are configured to intelligently cluster categorical data based on embeddings created by prompting a large language model (LLM). The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform embedding generation operations which include accessing a data set for categorical data, determining a row of the data set, generating a data container corresponding to the row and an instruction to the LLM that requests an embedding for the row, prompting the LLM to create the embedding using the data container, reducing a dimensionality of the embedding, and outputting the reduced dimensionality embedding to an ML training application executing for training an ML clustering model.