Dashboard Metadata Extraction for Natural Language Query Training
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Solution Overview
Problem
Conventional methods for generating training data for natural language querying in network management systems are inefficient due to repeated relabeling of data for different users and domain-specific terminologies, leading to increased time and computing resource usage.
Innovation Solution
The system automatically updates alias datasets by recording user interactions with performance data, determining user context, and generating alias data strings without manual labeling, allowing machine learning models to accurately process natural language queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual relabeling of training data is performed for each user and domain-specific terminology, then the LLM can accurately identify underlying database queries, but the time and computing resource usage increase significantly
Solution Approach 1:
The system automatically generates training data by extracting metadata from existing dashboards and widgets, eliminating the need for manual relabeling. The LLM processes this automatically generated training data to learn the mapping between natural language queries and database queries, making the system self-sufficient in maintaining training data accuracy across different users and domains.
Solution Approach 2:
A single automated training data generation process serves multiple users and domains simultaneously. The system extracts metadata from various dashboard sources and generates universal training data that can be used across different contexts, eliminating the need for separate manual relabeling processes for each user or domain.
2Measurement precision
If manual relabeling of training data is performed for each user and domain-specific terminology, then the LLM can accurately identify underlying database queries, but the computing resource usage increases significantly
Solution Approach 1:
The system automatically generates training data by extracting metadata from existing dashboards and widgets, eliminating the need for manual relabeling. The LLM processes this automatically generated training data to learn the mapping between natural language queries and database queries, making the system self-sufficient in maintaining training data accuracy across different users and domains.
Solution Approach 2:
The system extracts and copies metadata from existing dashboard configurations, widget definitions, and performance data structures to generate training examples. This copying approach reuses existing structured data to create training pairs, avoiding the computationally expensive process of manual annotation while preserving accuracy.
3Adaptability or versatility
If each model is trained on every customer's domain-specific networking data, then the model can handle customer-specific terminology, but the time and efforts required increase
Solution Approach 1:
A single automated training data generation process serves multiple users and domains simultaneously. The system extracts metadata from various dashboard sources and generates universal training data that can be used across different contexts, eliminating the need for separate manual relabeling processes for each user or domain.
Solution Approach 2:
The system performs preliminary extraction of domain-specific terminology and metadata from existing customer dashboards and configurations before model training. This pre-processing creates ready-to-use training data that incorporates customer-specific terminology, allowing the model to be trained efficiently without repeated manual preparation for each customer.
4Adaptability or versatility
If each model is trained on every customer's domain-specific networking data, then the model can handle customer-specific terminology, but the efforts and computing resource usage increase
Solution Approach 1:
A single automated training data generation process serves multiple users and domains simultaneously. The system extracts metadata from various dashboard sources and generates universal training data that can be used across different contexts, eliminating the need for separate manual relabeling processes for each user or domain.
Solution Approach 2:
The system extracts and copies metadata from existing dashboard configurations, widget definitions, and performance data structures to generate training examples. This copying approach reuses existing structured data to create training pairs, avoiding the computationally expensive process of manual annotation while preserving accuracy.
Data Source
AI summary
Systems, apparatuses, and methods for generating training data for machine learning models are disclosed. In an implementation training data for machine learning models can be created without requiring manual labeling and annotation of data. Natural language data from dashboards and widgets is extracted to identify user context. The user context is used to generate alias data strings that each correlate user context with natural language text. These alias data strings are used to train the machine learning model. Using user context derived from natural language text as basis to train the models, allows the system to generate accurate training data, without needing an end-user or system administrator to create a labeled set of data.