Natural Language Generation System Using Intermediate Configuration Data
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Solution Overview
Problem
Existing technologies face challenges in configuring natural language configuration data objects for use by large language models to generate natural language outputs representative of input data objects, particularly in efficiently processing and formatting data from various formats such as recurrent formal structure and natural language formats.
Innovation Solution
The proposed solution involves a natural language generation system that utilizes a configuration model to generate an intermediate natural language configuration data object, which is then processed by a synthesis model to create a natural language configuration data object. This object is specifically configured for use by a large language model to generate a natural language output representative of the input data object, accommodating formats like recurrent formal structure and natural language.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a configuration model generates an intermediate natural language configuration data object from input data objects in various formats, then the system can process diverse data formats, but the complexity of configuring and processing the intermediate configuration data object increases
Solution Approach 1:
The patent introduces an intermediate natural language configuration data object as a mediator between the input data object and the final natural language output. This intermediate object standardizes diverse input formats into a unified configuration structure that the synthesis model can process, thereby managing complexity while maintaining adaptability to various data formats.
Solution Approach 2:
The patent divides the natural language generation process into two distinct stages: a configuration model that creates the intermediate configuration data object, and a synthesis model that generates the final output. This segmentation allows each model to specialize in specific tasks, reducing the overall complexity of the system while handling diverse input formats effectively.
2Measurement precision
If a synthesis model processes the intermediate natural language configuration data object to generate natural language output, then the natural language generation accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The configuration model performs preliminary action by generating the intermediate natural language configuration data object before the synthesis model creates the final output. This preliminary structuring of data improves the accuracy of the final generation while allowing the synthesis model to focus on producing high-quality natural language output without redundant processing.
3Reliability
If the system configures natural language configuration data objects for use by large language models, then the quality of natural language output representative of input data objects improves, but the device complexity and configuration difficulty increase
Solution Approach 1:
The intermediate natural language configuration data object serves as a mediator that bridges the input data and the large language model. This intermediary structure ensures that the LLM receives properly formatted and contextualized configuration data, improving output quality while encapsulating the complexity within the configuration model rather than the overall system.
4Adaptability or versatility
If the system supports multiple input data formats including recurrent formal structure and natural language formats, then the versatility of the natural language generation system improves, but the difficulty of detecting and measuring input data characteristics increases
Solution Approach 1:
The configuration model is designed with multi-functionality to handle various input data formats including recurrent formal structures and natural language formats. It universally processes different formats and converts them into a standardized intermediate configuration data object, thereby maintaining versatility while simplifying subsequent processing through format unification.
Data Source
AI summary
Methods, apparatuses, and computer program products for a natural language generation system are described herein. An example method may include receiving, originating from a client computing device, an input data object. In some embodiments, the example method may include generating, based at least in part by applying a configuration model to the input data object, an intermediate natural language configuration data object. In some embodiments, the example method may include generating, based at least in part on applying a synthesis model to the intermediate natural language configuration data object, a natural language configuration data object. In some embodiments, the example method may include configuring the natural language configuration data object for use by a large language model in generating a natural language output representative of the input data object.