Bifurcated Image Reconstruction for LLM Numerical Extraction
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Solution Overview
Problem
Large Language Models (LLMs) are ineffective in processing numbers and detecting complex patterns involving numbers due to their reliance on probabilistic approximations rather than explicit rules or algorithms, and they treat numbers as sequences of characters, obscuring their quantitative significance.
Innovation Solution
The use of bifurcated image reconstructions, comprising a first reconstructed form with textual data and spatial coordinates, and a second reconstructed form with spatially-annotated text, along with custom prompts and instructions, enables LLMs to interpret and process numerical data accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs process numbers as sequences of characters, then they can handle numerical data input, but they lose the quantitative significance and cannot perform precise arithmetic or mathematical reasoning
Solution Approach 1:
The patent introduces an intermediary representation layer between the LLM and numerical data. Instead of feeding raw numbers directly to the LLM, the system first converts numerical data into a structured intermediate format that preserves quantitative relationships while being compatible with LLM processing. This intermediary representation allows the LLM to work with numbers without losing their mathematical meaning.
Solution Approach 2:
The system transforms the parameter representation of numerical data from character sequences to a format that explicitly encodes quantitative relationships. By changing how numbers are represented (from tokens to structured numerical formats with spatial annotations), the LLM can maintain both versatility in processing and precision in arithmetic operations.
2Adaptability or versatility
If LLMs rely on probabilistic approximations, then they can generate diverse outputs, but they cannot ensure logical consistency or rule-based computation
Solution Approach 1:
The patent segments the processing into distinct phases: a probabilistic generation phase that creates diverse outputs, and a subsequent validation phase that checks logical consistency using the structured intermediate representation. This segmentation allows the system to maintain both flexibility in output generation and reliability through systematic verification of logical constraints.
Solution Approach 2:
The system implements feedback loops where the structured intermediate representation is used to validate and correct LLM outputs. The feedback mechanism compares generated outputs against the structured numerical relationships, ensuring logical consistency while preserving the benefits of probabilistic generation.
3Adaptability or versatility
If LLMs treat numbers as character sequences, then they can process any numerical input format, but they cannot detect complex patterns involving quantitative relationships
Solution Approach 1:
The structured intermediate representation serves as a mediator that translates diverse input formats into a unified format that preserves quantitative relationships. This intermediary layer maintains input format flexibility while enabling effective pattern detection by presenting numerical data in a consistent, relationship-preserving structure to the LLM.
Solution Approach 2:
The system adds a new dimension to numerical data representation by incorporating spatial annotations and structured relationships. This dimensional enhancement transforms one-dimensional character sequences into multi-dimensional representations that encode quantitative relationships, making complex patterns detectable while maintaining input flexibility.
4Productivity
If LLMs use tokenization that obscures quantitative significance, then they can process natural language efficiently, but they cannot perform precise numerical reasoning
Solution Approach 1:
The patent segments the numerical processing from the general language processing pipeline. Numerical data is segmented out and converted into structured intermediate representations before being fed to the LLM, allowing efficient natural language processing to continue while numerical reasoning is handled through a specialized, precision-oriented pathway.
Solution Approach 2:
The system changes the parameter representation of numerical data from tokenized character sequences to structured formats that explicitly encode quantitative values and relationships. This parameter change maintains processing efficiency by using compact representations while dramatically improving numerical reasoning accuracy through preserved quantitative meaning.
Data Source
AI summary
Systems and methods for information extraction. As one example, systems and methods are described herein for image analysis and processing. The systems and methods achieve the improvements to information extraction through the use of Large Language Models (LLMs) through prompt construction that includes a plurality of reconstructed forms and instructions for processing each.