Corpus Generation With Structured Reasoning for Scenario-Specific AI Training
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Solution Overview
Problem
Large language models generate corpus content with semantic quality defects, making it difficult to apply to specific scenarios and accurately meet user intent, leading to inapplicable output and inefficient training.
Innovation Solution
Perform a content generation task using a large model with predetermined requirements, incorporating a reasoning process that includes multiple target tasks with dependency relationships, and determine target corpus data based on the corpus content and reasoning process information to enhance interpretability and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a large language model generates corpus content directly, then the generation process is simple and fast, but the semantic quality of the corpus content has defects and does not meet user intent accurately
Solution Approach 1:
The content generation process is divided into multiple independent target tasks with dependency relationships. Each task processes a specific aspect of content generation (e.g., understanding user intent, generating draft content, refining semantics, formatting output), allowing the system to improve semantic quality through structured processing while maintaining manageable complexity through modular task design.
2Manufacturing precision
If multiple target tasks with dependency relationships are used to improve corpus quality, then the reasoning process becomes more structured and accurate, but the processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary analysis of user requirements and task dependencies before executing the full content generation process. By pre-processing and planning the sequence of target tasks based on the specific requirements, the system avoids unnecessary processing steps and optimizes the execution path, reducing overall processing time while maintaining accuracy.
Solution Approach 2:
The system dynamically adjusts the number and depth of target tasks executed based on the complexity of the generation request. For simple tasks, fewer target tasks are performed; for complex tasks requiring high semantic quality, the full sequence of dependent tasks is executed. This adaptive approach balances processing time with accuracy requirements.
3Adaptability or versatility
If reasoning process information is included in the corpus data, then the interpretability and adaptability of the generated content improve, but the data structure becomes more complex
Solution Approach 1:
The system adds reasoning process information as a separate dimensional layer to the corpus data structure, rather than integrating it into the content itself. This creates a multi-dimensional data structure where the content and reasoning process are stored in different dimensions, allowing the system to maintain adaptability and interpretability while managing data structure complexity through dimensional separation.
Data Source
AI summary
A method for generating corpus data based on at least one large model is provided, which relate to the field of artificial intelligence technologies, and in particular to the fields of deep learning, large models, and intelligent question answering. The method includes: performing a content generation task by using the at least one large model based on a predetermined requirement condition to obtain a corpus content, where the content generation task includes a plurality of target tasks having dependency relationships, and the plurality of target tasks represent a reasoning process of the at least one large model for a corpus content to be generated; and determining target corpus data based on the corpus content and a reasoning process information related to the plurality of target tasks.