Agentic QA Pair Generation for Domain-Aligned AI Models
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Solution Overview
Problem
Current large language models (LLMs) and multimodal models (LMMs) lack domain-specific knowledge, leading to ethical, moral, and technical issues such as generating factually incorrect, toxic, or deceiving content, and failing to adhere to specific domain principles due to pre-training on incomplete or conflicting data, lacking clear understanding of domain-specific guidelines, and requiring complex prompts that increase computation time.
Innovation Solution
A framework is provided to align LLMs and LMMs with domain-specific principles through post-training or fine-tuning using domain-specific data and instructions, generated by an agentic workflow, ensuring adherence to ethical and operational guidelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs and LMMs are pre-trained on massive amounts of data to achieve general-purpose language understanding and generation, then their capability to model sequential data and generate human-like content is improved, but they lack domain-specific knowledge and generate factually incorrect or toxic content
Solution Approach 1:
The patent segments the training process into distinct phases: initial pre-training on general data to build foundational language capabilities, followed by domain-specific fine-tuning using curated datasets and reinforcement learning with human feedback (RLHF) to align with domain principles and eliminate harmful content
Solution Approach 2:
The patent applies different training quality standards to different domains by creating domain-specific training datasets with curated content that reflects the principles and requirements of each particular domain, rather than using uniform general-purpose training data
2Object-affected harmful factors
If LLMs use reinforcement learning with human feedback (RLHF) to reduce harmful content, then content safety is improved, but they still fail to fully adhere to domain-specific principles and require complex prompts
Solution Approach 1:
The patent performs preliminary alignment of the model with domain-specific principles through fine-tuning on curated datasets and RLHF before deployment, so that the model inherently understands and adheres to domain guidelines without requiring complex prompting during operation
Solution Approach 2:
The patent introduces domain-specific principle guidelines as an intermediary framework that mediates between the model's general language capabilities and domain-specific requirements, providing a structured set of principles that guide model behavior without requiring complex prompts
3Productivity
If foundation LLMs are generically pre-trained to perform well in broader context, then their general performance is improved, but they lack domain-specific organizational guidelines, standards, rules, intentions or values
Solution Approach 1:
The patent maintains continuity of useful action by building upon the foundational capabilities established during pre-training and continuously refining them through domain-specific fine-tuning and alignment processes, ensuring that general performance is preserved while adding domain-specific knowledge
Solution Approach 2:
The patent changes the training parameters and data distribution from general-purpose to domain-specific during fine-tuning, adjusting the model's weights and biases to reflect domain principles while maintaining the underlying language capabilities learned during pre-training
4Productivity
If LLMs are trained on incomplete or conflicting data, then training speed is improved, but they generate factually incorrect and deceiving content
Solution Approach 1:
The patent performs preliminary curation and validation of training data to ensure factual accuracy and consistency with domain principles before training, preventing the propagation of incorrect information while maintaining efficient training through careful data preparation
Solution Approach 2:
The patent implements feedback mechanisms during training including reinforcement learning with human feedback (RLHF) and fact-checking processes that identify and correct factual errors in model outputs, continuously improving accuracy while maintaining training efficiency
Data Source
AI summary
A question and answer (QA) pairs generation system and method uses an agentic workflow system and method to align generative artificial intelligence (a large language model (LLM) or a large multimodal model (LMM)) with the principles of a specific domain so that the generative artificial intelligence is better able to respond to a user query in the specific domain.