Multi-Domain Visual Agent Training Using Semantic Prompt Mapping
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Solution Overview
Problem
Artificial intelligence models face challenges in generating accurate outputs due to poor-quality training data, leading to hallucinations and difficulties in domain-specific training.
Innovation Solution
A computer-implemented method and system that leverage semantic information by sampling questions from question templates, mapping domain-specific labels to natural language descriptions, generating prompts, and using a large-language model to learn and train a multi-domain visual agent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI models are trained with poor-quality data, then training efficiency is maintained, but output accuracy deteriorates and hallucinations increase
Solution Approach 1:
The system performs preliminary actions by generating high-quality synthetic training data through simulated multi-domain interactions before actual model training. Question templates and domain-specific knowledge are prepared in advance to create structured training datasets, ensuring data quality is established before the training process begins
Solution Approach 2:
An intermediary layer of semantic information and structured question templates is introduced between the raw data and the AI model. This intermediary structures unstructured knowledge into standardized formats with explicit schemas, improving data quality without requiring extensive manual annotation
2Measurement precision
If domain-specific training is performed for each domain, then specialization accuracy improves, but system complexity and training time increase
Solution Approach 1:
A universal question template framework is designed that can adapt to multiple domains through parameter configuration rather than structural changes. The same base template system serves medical, legal, technical, and other domains by adjusting domain-specific vocabularies and knowledge bases, reducing overall system complexity
Solution Approach 2:
The training system is segmented into modular components: domain-agnostic template engines, domain-specific knowledge bases, and configurable parameter sets. This segmentation allows independent development and optimization of each component while maintaining overall system coherence across domains
3Measurement precision
If more training data is generated to improve model performance, then model accuracy improves, but computational resources and training time increase
Solution Approach 1:
Instead of collecting extensive real-world data, the system creates synthetic copies through structured question templates and simulated interactions. These synthetic training examples replicate the statistical properties and semantic structures of real data without requiring actual collection, reducing time and resource costs
Data Source
AI summary
Systems and methods for leveraging semantic information for a multi-domain visual agent. Semantic information can be leveraged to obtain a multi-domain visual agent. To train the multi-domain visual agent, questions can be sampled from question templates for domain-specific label spaces to obtain a unified label space. The domain-specific labels from the domain-specific label spaces can be mapped into natural language descriptions (NLD) to obtain mapped NLD. The mapped NLD can be converted into prompts by combining the questions sampled from the unified label space and the annotations. The semantic information can be learned by iteratively generating outputs from tokens extracted from the prompts using a large-language model (LLM). The multi-domain visual agent (MDVA) can be trained using the semantic information.