Document Query Retrieval Using Metadata-Guided Context Selection
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Solution Overview
Problem
Machine learning language models struggle to accurately generate responses to queries about documents when the number of words exceeds their context window size, leading to inaccurate responses due to the inability to utilize the entire document as context.
Innovation Solution
A data processing system that identifies metadata for a document, generates a retrieval command based on the query and metadata, and selectively retrieves relevant portions of the document using a machine learning model to generate a response, avoiding the need for human intervention and ensuring the context fits within the model's context window.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the entire document is used as context for generating a response, then the response may contain more complete information, but the document exceeds the machine learning model's context window size leading to inaccurate responses
Solution Approach 1:
The patent divides the entire document into multiple portions or segments. The machine learning model processes these segments individually or in controlled groups that fit within the context window size, rather than attempting to process the entire document at once. This segmentation allows the model to maintain accuracy while still accessing comprehensive document information through multiple processing steps.
Solution Approach 2:
The patent extracts only the relevant portions of the document that are necessary to answer the user's query, rather than using the entire document. This extraction process identifies and retrieves specific segments containing the most pertinent information, reducing the input size to fit within context window limitations while preserving the essential information needed for accurate response generation.
2Loss of information
If the entire document is processed by the machine learning model, then all information is considered, but the processing time increases and efficiency decreases
Solution Approach 1:
The patent extracts only the relevant portions of the document that are necessary to answer the user's query, rather than using the entire document. This extraction process identifies and retrieves specific segments containing the most pertinent information, reducing the input size to fit within context window limitations while preserving the essential information needed for accurate response generation.
Solution Approach 2:
The patent performs preliminary processing to identify and retrieve relevant document portions before passing them to the machine learning model. This pre-processing step includes analyzing the query, identifying relevant sections of the document, and preparing only those specific segments for model processing, thereby eliminating the need to process the entire document and significantly improving response generation efficiency.
3Measurement precision
If relevant document portions are selectively retrieved using automated methods, then the response accuracy improves by focusing on pertinent context, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary retrieval system that acts as a bridge between the user's query and the machine learning model. This intermediary component automatically identifies and retrieves relevant document portions based on the query, filtering out unnecessary information before it reaches the model. While this adds a component to the system, it simplifies the overall process by eliminating the need for manual information selection and ensuring the model receives only high-quality, relevant input.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for data processing include obtaining a query relating to a document and identifying metadata for the document based on the query, where the metadata describes a structure including a plurality of portions of the document. Some embodiments including generating, using a machine learning model, a retrieval command based on the query and the metadata, selectively retrieving at least one of the plurality of portions of the document based on the retrieval command, and generating, using the machine learning model, a response to the query based on the at least one of the plurality of portions of the document.