Domain-Specific Data Model Training via Knowledge Graph Automation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data models with broad response capabilities require extensive manual annotation and are labor-intensive and time-consuming to train, leading to insufficient accuracy in responses, especially for domain-specific knowledge.
Innovation Solution
A training system and method for domain-specific data models that utilize a domain knowledge graph to generate training datasets, incorporate reinforcement learning with a reward model, and automate evaluation to adjust model parameters, reducing manual intervention and improving response accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data models are trained with broad response capabilities using substantial textual content, then the model can respond to various inputs, but the training becomes labor-intensive and time-consuming due to manual annotation requirements
Solution Approach 1:
The system enables automated training data generation where the language model itself creates training samples with the help of knowledge graphs and evaluation models, eliminating the need for manual annotation. The model serves its own training needs by generating and evaluating its own training data through automated workflows.
Solution Approach 2:
Knowledge graphs serve as intermediaries between the language model and training data generation. The knowledge graphs provide structured domain knowledge that guides the automated creation of accurate training samples, mediating between the model's response capabilities and the training process.
2Adaptability or versatility
If data models generate responses based on probability, then they can produce varied outputs, but the accuracy of responses in specific domains is insufficient
Solution Approach 1:
The system applies domain-specific knowledge from knowledge graphs to specific training samples, ensuring that responses in particular domains achieve high accuracy. Instead of uniform training, the knowledge graphs provide localized, domain-specific guidance that enhances precision where needed.
Solution Approach 2:
The evaluation model provides feedback on the accuracy of generated responses by comparing them against knowledge graph information. This feedback loop enables the system to identify and correct inaccuracies, continuously improving response accuracy in specific domains through automated evaluation and retraining.
3Measurement precision
If manual annotation is used to ensure precision of model responses, then response accuracy improves, but the training process becomes labor-intensive
Solution Approach 1:
The system replaces manual annotation with automated training data generation where the language model creates its own training samples. The evaluation model automatically verifies precision against knowledge graphs, eliminating human labor while maintaining precision through automated feedback mechanisms.
Solution Approach 2:
The system substitutes the mechanical process of manual annotation with an automated computational system comprising knowledge graphs, evaluation models, and automated training data generation. This replaces human manual work with algorithmic processes that maintain precision while reducing complexity.
4Productivity
If regular updates are performed on data models to respond to incoming text inputs, then the model stays current, but the training process requires continuous manual intervention
Solution Approach 1:
The system enables continuous automated updating of the language model through automated training data generation and evaluation. The workflow operates continuously without manual intervention, with the evaluation model constantly assessing accuracy and triggering retraining when needed, maintaining the model's currency automatically.
Solution Approach 2:
The system performs self-updating through automated workflows where the language model generates its own training data, the evaluation model assesses its performance, and retraining occurs automatically when accuracy thresholds are not met. This self-service mechanism eliminates the need for continuous manual intervention while maintaining high update frequency.
Data Source
AI summary
A training system and a training method for a domain-specific data model are provided. The training method includes configuring a computing device to perform the following processes: generating, by a training set generation module, a training data set based on a domain knowledge graph; updating the data model based on the training data set; generating, by the training set generation module, training input text corresponding to the domain knowledge graph; inputting the training input text into the data model to obtain training output text; evaluating and generating a score by an evaluation module based on a correlation between the training output text and the domain knowledge graph; and adjusting, by a reinforcement learning module, parameters of the data model according to the score and an optimization goal of the reward model until the score meets a training completion condition, taking the data model as the domain-specific data model.