Phone and Address Encoders Using Contrastive Geographic Embeddings
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Solution Overview
Problem
Existing language models struggle to effectively encode phone numbers and addresses, failing to leverage their geographical characteristics, which limits their utility in tasks like fraud detection and LLM enhancement.
Innovation Solution
A system that trains separate encoders for phone numbers and addresses using contrastive learning to minimize distance between related vectors, enabling geographical embedding representations that can be used in conjunction with LLMs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If language models are used to encode phone numbers and addresses, then semantic meanings can be captured, but geographical characteristics and inert properties are lost
Solution Approach 1:
The patent divides the encoding task into separate models: a language model for semantic encoding and a dedicated encoder for geographical/inert property encoding. This segmentation allows each model to specialize in its specific function, preventing information loss while maintaining versatility across multiple tasks.
Solution Approach 2:
The patent merges the outputs of the language model and the dedicated encoder into a unified embedding representation. This combination integrates both semantic meanings and geographical characteristics into a single structure that can be used across multiple tasks including fraud detection, entity matching, and location-based services.
2Reliability
If classical machine learning models are used, then geographical features can be encoded, but the multiple advantages of LLMs such as language understanding are lost
Solution Approach 1:
The patent creates a universal encoding system where the language model handles both semantic encoding and geographical encoding through its encoder-decoder architecture. This single model can process various input types (phone numbers, addresses, locations) and produce appropriate embeddings for different tasks, eliminating the need for separate specialized models while maintaining high reliability in geographical feature representation.
3Productivity
If phone numbers and addresses are encoded using language models, then processing speed is maintained, but encoding accuracy for geographical features deteriorates
Solution Approach 1:
The patent introduces a dedicated encoder as an intermediary component that specifically processes geographical features and phone numbers to produce accurate embeddings. This intermediary layer bridges the gap between fast language model processing and precise geographical encoding, allowing the system to maintain both speed and accuracy by having the dedicated encoder handle the precision requirements while the language model provides the speed advantage for semantic processing.
Data Source
AI summary
An address encoder and a phone number encoder can be trained on training data including an address dataset, a phone number dataset, and information associating respective addresses in the address dataset with respective phone numbers in the phone number dataset as associated pairs. The training can be by a constrastive learning process such that respective distances between respective pairs of address vectors from the trained address encoder and phone number vectors from the trained phone number encoder are minimized for respective associated pairs. In production, the trained encoders can determine a production distance between a production address and a production phone number, and these results can be used to modify a production computing process.