Hybrid LLM and Machine Learning for Mixed Document Analysis
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Solution Overview
Problem
Existing systems struggle to effectively analyze mixed data comprising both textual and numeric features, as machine learning is ineffective with textual data and large language models are less effective with structured numerical data.
Innovation Solution
A system combining machine learning techniques with large language models to analyze mixed data, extracting features from documents, using LLMs for natural language analysis and machine learning for numerical data, and integrating the outputs to make informed decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning is used to analyze numerical data, then analysis accuracy for numerical features is improved, but effectiveness with textual natural language data deteriorates
Solution Approach 1:
The patent combines machine learning models and large language models into a unified hybrid system that processes mixed data types. The machine learning component handles numerical features while the LLM component handles textual features, and their outputs are integrated to produce a comprehensive analysis result, thereby resolving the contradiction between numerical analysis accuracy and textual data effectiveness.
Solution Approach 2:
The hybrid system is designed to handle multiple data types (numerical and textual) within a single framework. By making the system multi-functional, it can effectively process mixed data comprising both numerical and natural language features, overcoming the limitation of machine learning models that are specialized only for numerical data.
2Measurement precision
If large language models are used to handle textual natural language data, then analysis accuracy for textual features is improved, but effectiveness with structured numerical data deteriorates
Solution Approach 1:
The patent merges LLMs and machine learning models in a hybrid architecture where each component processes its suitable data type. The LLM processes textual features with high accuracy while the machine learning model processes numerical features, and their results are combined to achieve comprehensive analysis capability.
Solution Approach 2:
The hybrid system achieves universality by integrating two specialized models, enabling it to handle both textual and numerical data effectively within a single system, thus overcoming the limitation of LLMs that are specialized only for textual data.
3Device complexity
If a single model is used to process mixed data, then system complexity is reduced, but analysis performance on both textual and numerical features deteriorates
Solution Approach 1:
The patent segments the data processing task into two specialized components: one for numerical data and one for textual data. By dividing the processing workload according to data type, each component can be optimized for its specific function, achieving high analysis performance while maintaining manageable system complexity through modular design.
Data Source
AI summary
A computing system is disclosed that utilises a large language to provide scalar indications of characteristics of a document, and a decision-making system to take decisions regarding handling of that document in view of the scalar indications. In one embodiment, one or more computer readable storage media storing program instructions and one or more processors which, in response to executing the program instructions, are configured to: receive a document; extract textual data from the document; request a large language model to provide a scalar indication for each of a plurality of features of the textual data; and utilise a decision system to produce an output based on at least the scalar indications.