LLM Prompting for Multidimensional Anomaly Summaries
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Solution Overview
Problem
Existing data visualization systems lack dynamic and context-aware notification triggers for multidimensional data anomalies, requiring users to manually set static threshold values, which can lead to inefficiencies and missed alerts.
Innovation Solution
A computer-implemented method using large language models (LLMs) to generate summaries of anomalies by prompting with key-value pairs from multidimensional data, including drill-down dimensions and time variance comparisons, to enrich the summary without overwhelming the model with extraneous information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static notification triggers with manual threshold values are used, then users can configure alerts for specific conditions, but the system lacks dynamic adaptability and requires user understanding of threshold values ahead-of-time
Solution Approach 1:
The system automatically detects anomalies and generates notification triggers without requiring user configuration of threshold values. The AI model autonomously analyzes multidimensional data patterns, identifies anomalies, and creates context-aware notifications, allowing the system to serve itself rather than requiring user intervention for threshold setup
Solution Approach 2:
The system dynamically adjusts notification parameters based on real-time data analysis. Instead of fixed user-defined thresholds, the AI model continuously learns from data patterns and adapts anomaly detection parameters automatically, enabling dynamic adaptability while maintaining ease of operation
2Measurement precision
If LLM is provided with comprehensive multidimensional data, then the summary accuracy improves, but the model may be overwhelmed with extraneous information
Solution Approach 1:
The system extracts only the most relevant key-value pairs from multidimensional data that are directly related to detected anomalies. By filtering and selecting only essential dimensions and members that contribute to anomaly context, the system provides sufficient information for accurate summarization while avoiding overwhelming the LLM with unnecessary data
Solution Approach 2:
The multidimensional data is segmented into hierarchical levels (aggregate level and detailed member level). The system selectively provides aggregate-level context and drills down to detailed member-level information only when relevant to the specific anomaly, organizing information in manageable segments that improve accuracy without overwhelming complexity
Data Source
AI summary
Systems, methods, and computer-readable media are provided for detecting an anomaly involving multiple dimensions, and generating a summary of the anomaly at least in part by prompting an LLM with key-value pairs relevant to the anomaly. The key-value pairs provided may be determined by drilling down into dimensional members most relevant to the anomaly (e.g., Top N and/or Bottom N members) to provide context for the LLM to summarize the anomaly and account for various levels in a multidimensional hierarchy. The key-value pairs may additionally or alternatively be determined by comparing values from different times relevant to the anomaly to provide context for the LLM to summarize the anomaly and account for relevant time variances. The key-value pairs of the Top N and/or Bottom N members and/or time variant comparison values may be included to enrich the LLM's summary to account for the multidimensional hierarchy and/or relevant time variances without overwhelming the LLM with extraneous information.