Automated NLP Pipeline Generation via Language Model
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Solution Overview
Problem
Existing techniques for designing natural language processing (NLP) pipelines require manual writing or modification of program code, making them inefficient and labor-intensive, especially when processing different data sets.
Innovation Solution
A computer-implemented method for generating data processing pipelines, including NLP pipelines, that uses predefined services associated with user input, allowing pipelines to be created without writing code. This method involves receiving user input via a user interface and generating a pipeline using a language model that processes natural language text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual code writing is used to design NLP pipelines, then the pipeline can be customized and controlled, but the development time and labor intensity increase significantly
Solution Approach 1:
The system enables self-service by allowing users to generate NLP pipelines through natural language descriptions without requiring manual code writing. The pipeline generator automatically translates user intent into functional pipelines, eliminating the need for developers to write code manually while maintaining customization capabilities.
Solution Approach 2:
The patent replaces the mechanical process of manual code writing with an automated language model-based system. Instead of requiring developers to manually write and modify code, the system uses natural language processing to automatically generate pipelines, substituting the mechanical coding process with an intelligent automated approach.
2Adaptability or versatility
If manual code modification is used to adapt pipelines to different data, then the pipeline can be precisely tailored, but the effort and time required increase
Solution Approach 1:
The system enables self-service adaptation by automatically generating pipelines tailored to different data types through natural language descriptions. Users simply describe their needs in natural language, and the system automatically adapts the pipeline configuration without requiring manual modification efforts.
Solution Approach 2:
The pipeline generator creates universal pipeline templates that can adapt to multiple different data types and processing requirements. A single system can handle various NLP tasks and data formats by interpreting natural language descriptions, providing multi-functionality without requiring separate custom code for each scenario.
3Ease of operation
If predefined services are used to generate pipelines, then the ease of operation improves, but the level of automation decreases compared to fully automated generation
Solution Approach 1:
The system uses natural language as an intermediary between user intent and pipeline configuration. Instead of requiring direct interaction with code or technical parameters, users communicate through natural language, which the language model translates into automated pipeline generation, bridging the gap between ease of use and automation.
Solution Approach 2:
The patent replaces manual mechanical operations of selecting and configuring predefined services with an automated language model that directly generates pipelines from natural language descriptions. This substitution maintains ease of operation while significantly increasing the extent of automation.
Data Source
AI summary
Techniques for generating data processing pipelines include receiving user input via a user interface, and generating, based on the user input, a data processing pipeline that includes a set of predefined services, wherein the set of predefined services are associated with the user input.