LLM Ensemble Attribute Extraction for Product Image Size Verification
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Solution Overview
Problem
Current systems inaccurately identify and catalog item attributes, such as size and count information, when extracting product information from images.
Innovation Solution
An online system employs a multi-step ensemble method using multi-modal large language models to verify and confirm the presence of size and count information, combining multiple large language models to determine and confirm the presence of size and count information, and then extract the value of size or count information, and then update the item attribute data with the matching values of size or count information attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single language model is used to extract item attributes, then the processing speed is fast, but the accuracy of attribute identification is low
Solution Approach 1:
The patent segments the attribute extraction process into multiple independent steps: first determining whether size or count information is present, then extracting the specific values. This segmentation allows the system to apply different processing strategies to different aspects of attribute extraction, improving overall accuracy while maintaining efficiency.
Solution Approach 2:
The patent performs preliminary determination of whether size or count information exists before attempting extraction. This preliminary action prevents wasted processing on irrelevant attributes and allows the system to focus computational resources on relevant extraction tasks, thereby improving both accuracy and efficiency.
2Reliability
If multiple language models are used to verify attribute information, then the accuracy improves, but the computational cost increases
Solution Approach 1:
The patent employs a threshold-based verification mechanism where attribute extraction is confirmed only when a specified number of models agree on the presence of size or count information. This partial verification approach achieves sufficient reliability without requiring all models to process every attribute, thereby reducing computational cost while maintaining extraction reliability.
3Manufacturing precision
If detailed verification of size and count information is performed, then the extraction precision improves, but the processing time increases
Solution Approach 1:
The patent divides the verification process into distinct stages: first verifying the presence of size or count information, then extracting specific values only when presence is confirmed. This segmentation prevents unnecessary processing time being spent on attributes that are not present, thereby maintaining extraction precision while reducing overall processing time.
Solution Approach 2:
The system performs preliminary verification of attribute presence before conducting detailed value extraction. This preliminary action filters out cases where size or count information is absent, preventing wasted processing time on irrelevant extractions while ensuring precision is maintained for relevant attributes.
Data Source
AI summary
An online system automatically identifies item attributes of an item. The online system prompts a set of outputs from a set of multi-modal large language models with an image of the product and a request to determine if the details of size information is present in the image. The online system receives a set of outputs, wherein an output describes whether the size information is present in the image. The system then prompts the set of language models with a request to extract the value of the size information in the image. Responsive to determining that a threshold number of outputs have matching values of size information that is present in the image, the system updates the item attribute data with the matching values of size information of the product.