Automated Multi-Round Dialogue Data Generation via LLM Dependency
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Solution Overview
Problem
Traditional chatbots have limited comprehension abilities regarding user queries, leading to unsatisfactory responses due to the scarcity of dialogue data samples used to train dialogue content generation models, which are inefficient and costly to manually annotate.
Innovation Solution
A method for generating dialogue data that automatically produces multi-round dialogue data for various scenarios within a target domain by obtaining prompt templates and generation content dependency relationships for human-machine dialogue elements, and progressively generating content using a pre-trained large language model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation methods are used to generate dialogue data samples, then the quality and accuracy of training data can be ensured, but the efficiency is low and the cost is high
Solution Approach 1:
The system enables automated self-generation of dialogue data through large language models, where the model generates dialogue content without human intervention. The framework includes automated quality control mechanisms that allow the system to self-evaluate and refine generated data, eliminating dependency on manual annotation while maintaining data quality standards.
Solution Approach 2:
The invention changes the fundamental parameter of data generation from manual human annotation to automated algorithmic generation using large language models. By adjusting model parameters such as temperature, top-k sampling, and repetition penalties, the system can control the quality and diversity of generated dialogue data, achieving both high efficiency and acceptable quality levels.
2Measurement precision
If manual annotation methods are used to generate dialogue data samples, then the accuracy of training data can be maintained, but the cost and time consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-training large language models on extensive dialogue corpora before actual data generation. This preliminary training enables the model to understand dialogue patterns and generate accurate responses without requiring time-consuming manual annotation for each specific dialogue sample, significantly reducing data acquisition time while maintaining accuracy.
3Ease of manufacture
If traditional chatbots use limited dialogue data samples, then the manual annotation cost is reduced, but the comprehension ability and response quality deteriorate
Solution Approach 1:
The invention creates a universal dialogue data generation framework that can produce diverse dialogue samples across multiple domains and scenarios. The large language model serves multiple functions: generating user queries, creating appropriate responses, maintaining dialogue context, and ensuring logical consistency. This multi-functional system dramatically increases both the quantity and quality of training data available for chatbot development.
Data Source
AI summary
The application provides a method for generating dialogue data, a method for training a model, and a method for processing dialogues. The dialogue data generation method includes: obtaining a prompt template and a generation content dependency relationship corresponding to each of human-machine dialogue elements in a target scenario, wherein the human-machine dialogue elements at least include: user queries and response content; progressively generating generation content of the corresponding human-machine dialogue elements based on the prompt templates and a pre-trained large language model according to the generation content dependency relationships; generating multi-round dialogue data in the target scenario based on the generation content respectively corresponding to the user queries and the response content. This method enables the fully automatic generation of multi-round dialogue data in a target domain, eliminating the need for manual annotation, reducing the cost of acquiring multi-round dialogue data, and improving the efficiency of dialogue data acquisition.