Interactive Graphical Interface for Verified Multi-Modal Data Summaries
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Solution Overview
Problem
Current data exploration techniques for risk analysis, such as financial fraud detection and medical diagnostics, are complex, time-consuming, and prone to information overload, with existing visual and textual summarization methods often leading to misleading insights due to inaccurate Large Language Model (LLM) outputs and high computational costs.
Innovation Solution
A computer-implemented method and system that generates an interactive graphical user interface combining Retrieval-Augmented Generation (RAG) with pre-defined Knowledge Areas (KAs) to provide multi-modal summaries, leveraging automated textual insights and graphical analytics, while mitigating hallucinations through prompt engineering and hallucination detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If LLMs are used to generate text summaries of data, then users can gain early advantage in analysis through summarized insights, but the summaries may include misleading or deceptive conclusions due to hallucinations
Solution Approach 1:
The system incorporates a verification mechanism that checks LLM-generated text summaries against the actual data and allows users to confirm or correct the information. This feedback loop enables users to verify accuracy while maintaining the time efficiency of automated summarization.
Solution Approach 2:
The system introduces an intermediary verification layer between the LLM and the user, where generated summaries are presented with indicators of their reliability and can be verified against the source data before being fully accepted by the user.
2Ease of operation
If multiple graphical representations are used to visually encode each data attribute, then users can easily retrieve insights through human perception, but the data exploration process becomes overwhelming
Solution Approach 1:
The system segments the data exploration interface into multiple panels, each dedicated to specific data attributes or insights. This segmentation allows users to access individual attributes through simple selections while the system automatically generates relevant graphical representations, reducing the overall complexity.
Solution Approach 2:
The system automatically generates and adjusts graphical representations based on the data and user selections, eliminating the need for users to manually create complex visualizations. The system serves itself by adapting the visual interface to the current data state.
3Reliability
If automated systems provide risk estimates and risk-attribution insights, then analysts can make more informed decisions, but the task complexity increases as analysts must incorporate multiple systems' insights
Solution Approach 1:
The system merges automated risk estimation, risk-attribution, and data exploration capabilities into a unified interface. By combining these functions and presenting their insights together in a coordinated manner, the system reduces the complexity of integrating multiple separate systems.
Solution Approach 2:
The system creates a universal analysis platform that handles multiple types of insights (risk estimates, attributions, data patterns) through a single cohesive interface, allowing analysts to work with all types of information using the same tools and workflows.
Data Source
AI summary
An interactive graphical user interface is generated and provided. Transactional data records are received, each comprising a plurality of data attributes. Using the data attributes, each received data record is classified into zero, one or more classes of a first classification. Each previously classified data record is classified into zero, one or more classes of a second classification, using the data attributes and the first classification. A user selection of a class of the first classification is received and a text summary for the received data records having been attributed the selected class is generated. The text summary is fed to the graphical user interface. The text summary includes the class or classes attributed by the second classification attributed to the received data records having been attributed the selected class. A graphic representation for the selected class is generated.