Role-Based LLM Dialogue Planning for High-Relevance Corpus Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large language models generate corpus content with semantic quality defects, making it difficult to accurately satisfy user requirements.
Innovation Solution
Conduct a dialogue on a predetermined topic using a plurality of role-based large models, perform dialogue strategy planning with a designated large model to constrain the speaking pattern, and determine target corpus data based on the generated utterance content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If large language models generate corpus content directly, then generation speed is fast, but semantic quality is poor and fails to satisfy user requirements accurately
Solution Approach 1:
The corpus generation process is segmented into multiple independent modules: topic analysis module, dialogue strategy generation module, dialogue execution module, and corpus construction module. Each module performs a specific function, with the topic analysis module breaking down the input topic into key elements, the strategy generation module creating constraints based on those elements, the dialogue execution module generating content under constraints, and the corpus construction module assembling final output. This segmentation allows fast generation while maintaining quality control at each stage.
Solution Approach 2:
A dialogue strategy is introduced as an intermediary element between the topic analysis and dialogue execution. The strategy acts as a mediator that translates user requirements into concrete constraints for the dialogue process, ensuring that the generated corpus content aligns with semantic quality requirements while maintaining generation efficiency. The strategy includes speaking pattern constraints, topic adherence rules, and quality control parameters.
2Adaptability or versatility
If multiple role-based large models conduct dialogue without constraints, then corpus diversity is high, but semantic relevance to topic deteriorates
Solution Approach 1:
Different constraints are applied to different aspects of the dialogue process based on local requirements. The dialogue strategy generates specific speaking pattern constraints for different roles (e.g., expert role requires authoritative tone, conversational role requires friendly tone), topic-specific constraints for maintaining relevance, and quality control constraints for ensuring accuracy. This local quality approach allows diverse expressions within each role while maintaining overall semantic relevance to the topic.
3Manufacturing precision
If dialogue strategy planning is performed to constrain speaking patterns, then semantic quality improves, but system complexity increases
Solution Approach 1:
The dialogue strategy transforms qualitative semantic requirements into quantitative parameters that can be processed algorithmically. Speaking patterns are defined by parameters such as tone intensity, formality level, and sentence structure complexity. Topic adherence is measured by parameter-based similarity metrics. Quality control uses numerical thresholds for fact-checking and consistency verification. This parameterization reduces system complexity by converting abstract semantic constraints into concrete, computable conditions.
Data Source
AI summary
A method for generating corpus data based on large models is provided, which relates 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: conducting a dialogue on a predetermined topic by using a plurality of role-based large models to obtain an utterance content of at least one of the role-based large models; performing dialogue strategy planning based on the utterance content by using a designated large model to obtain a dialogue strategy, where the dialogue strategy constrains a speaking pattern of the role-based large models during a dialogue process; and determining target corpus data related to the predetermined topic according to a target utterance content, where the target utterance content is generated by the role-based large models conducting a dialogue based on the dialogue strategy.