LLM Prompting for Hierarchical Financial Anomaly Explanations
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Solution Overview
Problem
Determining when multi-dimensional data represents an important datapoint and describing the results of multi-dimensional data analysis can be difficult, requiring in-depth and constant review and analysis by a user to convey key takeaways effectively.
Innovation Solution
A computer-implemented method that generates natural language summaries of multi-dimensional data anomalies by prompting a Large Language Model (LLM) with a prompt including a path to a member of the hierarchy, delimiter, and metrics defining the anomaly, allowing for the LLM to understand the ancestral context and generate a summary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multi-dimensional data analysis is performed to discover patterns and insights, then the quality and depth of insights improve, but the complexity of interpreting and conveying results increases
Solution Approach 1:
The patent introduces an intermediary system that automatically generates natural language explanations for multi-dimensional data analysis results. This intermediary translates complex analytical findings into easily understandable narratives, bridging the gap between sophisticated data analysis and user comprehension without requiring users to directly interpret complex multi-dimensional data structures.
Solution Approach 2:
The system employs large language models to autonomously generate explanations and insights from multi-dimensional data analysis. The LLM independently processes the analytical results, formulates coherent narratives, and presents findings without requiring manual interpretation or synthesis by users, thereby eliminating the complexity burden from end-users.
2Measurement precision
If manual review and analysis of multi-dimensional data is conducted to convey key takeaways, then the accuracy of insights improves, but the time and effort required increase
Solution Approach 1:
The patent replaces the mechanical process of manual data review and analysis with an automated computational system based on large language models. The LLM automatically performs the analytical work, generates insights, and formulates explanations, substituting human manual effort with an efficient automated mechanism that maintains accuracy while dramatically reducing time investment.
Solution Approach 2:
The system performs preliminary analysis and explanation generation automatically before users need the insights. By pre-processing the multi-dimensional data and generating ready-to-use explanations in advance, the system eliminates the need for users to invest time in manual review, delivering pre-digested, accurate insights immediately when needed.
3Reliability
If detailed multi-dimensional data analysis is performed to identify important data points, then the reliability of findings improves, but the difficulty of describing results increases
Solution Approach 1:
The patent introduces a natural language generation intermediary that translates reliable but complex analytical findings into easily describable formats. This intermediary layer maintains the reliability of detailed analysis while automatically converting results into clear, concise descriptions that are easy to communicate and understand, eliminating the difficulty of result description.
Solution Approach 2:
The system changes the parameter of result presentation from complex technical formats to natural language descriptions. By transforming the output format while preserving the underlying analytical reliability, the system makes findings both reliable and easily describable, allowing users to communicate insights without struggling with complex data structures or technical terminology.
Data Source
AI summary
Systems, articles, and computer-implemented methods are disclosed for generating natural language summaries of a multi-dimensional analysis of a detected anomaly within a member of multi-dimensional data by prompting a LLM with a prompt generated to include data about the anomaly in a manner understandable by the LLM. The prompt to the LLM includes a path to a member of the hierarchy containing an anomaly with a delimiter between the member and ancestor nodes. The delimiter allows the ancestral context of the member of the hierarchy to be understood by the LLM. The prompt also includes a metric defining a magnitude of the anomaly in relation to another value, such as an average, a value of the anomaly, a time corresponding to the anomaly, and one or more examples of other anomalies with included data about those anomalies matching the type of data provided for the detected anomaly.