ML Fact Checking With Contradiction Retrieval in Document Clusters
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Solution Overview
Problem
Conventional methods for identifying contradicting documents and statements in large document sets, such as during a deposition, are time-consuming and resource-intensive, requiring manual searching through extensive amounts of text or documents.
Innovation Solution
A system utilizing machine learning models, particularly large language models, to encode documents into mathematical vector representations (embeddings) for clustering and semantic relevance, followed by hierarchical and parallel processing to efficiently identify documents and statements that contradict given text data, leveraging GPUs and modern hardware for optimized computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual searching through extensive documents is used to identify contradicting documents, then accuracy can be maintained, but time consumption and resource requirements increase significantly
Solution Approach 1:
The patent replaces manual mechanical searching with machine learning models that automatically analyze documents. The system uses natural language processing and contradiction detection algorithms to identify contradicting documents without human intervention, thereby maintaining accuracy while dramatically reducing time consumption.
Solution Approach 2:
The system performs self-service by automatically processing, analyzing, and identifying contradicting documents within the document set. The machine learning models independently execute the fact-checking process, eliminating the need for manual searching and enabling rapid automated contradiction detection.
2Measurement precision
If comprehensive document analysis is performed to ensure accuracy, then measurement precision improves, but computational resources and processing time increase
Solution Approach 1:
The patent applies local quality by focusing computational resources on specific document regions and relationships that are most likely to contain contradictions. The machine learning models prioritize analysis of documents with high semantic similarity to the input text, rather than uniformly processing all documents, thereby reducing overall computational resource consumption while maintaining accuracy.
Solution Approach 2:
The system dynamically adjusts processing parameters based on document characteristics and complexity. The machine learning models adapt their analysis depth and computational intensity according to the specific document content, allowing efficient resource allocation that maintains accuracy while minimizing unnecessary computational expenditure on straightforward documents.
3Speed
If rapid document processing is implemented to meet tight time constraints, then speed improves, but computational complexity and processing requirements increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and indexing documents before contradiction detection is needed. The system pre-organizes document metadata, creates semantic representations, and prepares contradiction detection models, enabling rapid processing during time-constrained operations without increasing computational complexity during actual analysis.
Solution Approach 2:
The system segments the document analysis process into independent, parallelizable tasks that can be executed simultaneously. The machine learning models divide the contradiction detection process into discrete processing stages, allowing rapid parallel computation that maintains high processing speed while managing computational complexity through efficient task decomposition.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing tasks. One of the methods includes receiving a trigger from a user; responsive to the trigger, obtaining text data representing one or more subwords to be processed; obtaining data representing a plurality of clusters, wherein each cluster comprises one or more documents of a plurality of documents; processing the text data to identify one or more clusters of the plurality of clusters that are relevant to the text data; for each of the one or more identified clusters: identifying one or more documents of the identified cluster that are relevant to the text data; identifying one or more documents that contradict the text data of the one or more identified documents that are relevant to the text data; and providing data representing the one or more identified documents that contradict the text data.