Multistage Learning for Base Model Generation
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Solution Overview
Problem
Existing model generation technologies face limitations in learning and generating models, particularly for base models and fine-tuned models, which lack flexibility and efficiency in handling industry-specific expressions and varied applications.
Innovation Solution
An information processing method that involves obtaining learning data for a base model and performing a multistage learning operation to generate the base model, allowing for optimized generation and adaptation to specific tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a model is generated by learning features of learning data for various types of models, then accurate interpretation of industry-specific expressions is enabled, but flexibility in generating base models and fine-tuned models for various applications is limited
Solution Approach 1:
The learning process is divided into multiple stages: a first learning stage for generating a base model from general learning data, and a second learning stage for fine-tuning the base model into task-specific models using task-specific learning data. This segmentation allows the system to achieve both accurate interpretation of industry-specific expressions through fine-tuning and the flexibility to generate different types of models for various applications by controlling the base model generation phase.
2Adaptability or versatility
If a base model is generated to be applicable to various applications, then versatility is improved, but the learning process becomes more complex
Solution Approach 1:
The learning process is segmented into two distinct stages: first, generating a base model from general learning data that captures universal language patterns; second, fine-tuning the base model with task-specific learning data for particular applications. This segmentation reduces overall complexity by breaking down the complex task of creating versatile models into manageable sequential steps, where each stage has clear objectives and inputs.
Solution Approach 2:
The base model is generated in advance through the first learning stage before any task-specific fine-tuning occurs. This preliminary action creates a universal foundation that can be subsequently adapted to various applications through the second learning stage, simplifying the overall process by preparing general capabilities beforehand rather than training separate models for each application.
3Ease of manufacture
If learning data is processed in a single stage, then the process is simpler, but the model cannot be optimized for both general understanding and specific task performance
Solution Approach 1:
The learning process is divided into two sequential stages: the first learning stage processes general learning data to create a base model with general understanding capabilities; the second learning stage processes task-specific learning data to optimize the base model for specific applications. This segmentation enables the system to achieve both process simplicity (through clear sequential steps) and manufacturing precision (through targeted optimization in the second stage).
Solution Approach 2:
The base model is generated in advance through the first learning stage as a preliminary action, establishing general understanding capabilities before task-specific optimization. This preliminary generation simplifies the overall process by creating a ready-to-use foundation that can be efficiently fine-tuned for specific tasks in the second stage, rather than requiring complex single-stage processing.
Data Source
AI summary
An information processing method includes: obtaining the learning data which is to be used in the learning of a base model that treats texts as the input; and performing a multistage learning operation with the use of the learning data, and generating the base model.