Labeled Knowledge Graph Priming for Natural Language Models
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Solution Overview
Problem
Existing natural language user interfaces face suboptimal performance due to suboptimal priming of natural language models, often resulting from developer-provided examples that are ambiguous, overlapping, and imprecise, especially when application programs are constructed from independently developed reusable components.
Innovation Solution
Utilizing a labeled knowledge graph to generate optimized natural language input examples that effectively prime the existing natural language model, by parsing triples and concatenating labels to create precise and distinct examples that align with the model's understanding, thereby improving the accuracy of user input processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If developer-provided natural language examples are used to prime the existing natural language model, then the application program can receive and process natural language input, but the examples are often suboptimal, ambiguous, and overlapping, which degrades the natural language input processing performance
Solution Approach 1:
The system performs preliminary action by automatically generating optimized natural language examples from the knowledge graph before the natural language processing occurs. These pre-generated examples serve as superior priming material for the existing natural language model, replacing the suboptimal developer-provided examples and establishing an accurate baseline for comparison during runtime.
Solution Approach 2:
The system introduces an intermediary component - the automated example generation system that uses the knowledge graph as a structured intermediary between the application program's functionality and the natural language model. This intermediary translates programmatic concepts into precise natural language examples, bridging the gap between developer intent and model understanding without relying on ambiguous developer-provided examples.
2Adaptability or versatility
If examples from multiple independently developed components are combined, then the application program can utilize reusable components, but the examples overlap and conflict, causing ambiguity in natural language input processing
Solution Approach 1:
The system merges the example generation functionality across all independently developed components into a unified process. Instead of allowing each component to provide separate, potentially conflicting examples, the system consolidates the knowledge graphs from all components and generates a unified set of optimized natural language examples that are consistent across the entire application program, eliminating overlaps and conflicts.
Solution Approach 2:
The system changes the parameters of example generation by transitioning from component-level independent example provision to application-program-level unified example generation. This parameter change transforms the granularity and scope of example creation, ensuring that examples are generated with consistent terminology and phrasing across all components while maintaining their individual functionalities.
3Ease of operation
If casual and informal language is used in natural language examples, then the examples are more natural and easier to understand, but the language becomes imprecise and ambiguous, reducing processing accuracy
Solution Approach 1:
The system applies local quality by generating natural language examples that are contextually appropriate and domain-specific rather than using generic casual language. The knowledge graph provides structured, domain-relevant terminology that is tailored to each specific application program's functionality, ensuring that the language is both precise for accurate processing and natural for user understanding within that specific context.
Data Source
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AI summary
A natural language model can be primed utilizing optimized examples generated from a labeled knowledge graph corresponding to an independently developed application program. Parsing of the labeled knowledge graph can include the identification of triples, comprising a source node, a destination node, and a link between them, each of which can be labeled. One or more natural language input examples can be generated from an individual triple by concatenating the natural language words or phrases utilized to label the source node in the link. Determinations that subsequently received natural language user input is similar to the generated examples can result in an identification of the triple, which can, in turn, trigger the performance of a function associated with the destination node of the triple. Labels can include preferred labels and alternative labels, and various permutations thereof can be concatenated to generate alternative natural language input examples.