Agentic AI Model Alignment for Domain-Specific Accuracy
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Solution Overview
Problem
Current large language models (LLMs) and multimodal models (LMMs) lack domain-specific knowledge, leading to generation of factually incorrect, toxic, or deceiving content, and fail to adhere to specific domain principles due to pre-training on incomplete or conflicting data, necessitating alignment with domain-specific guidelines.
Innovation Solution
A system and method for aligning 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 compliance with ethical and regulatory standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs are pre-trained on massive amounts of general data, then they acquire general language understanding and generation abilities, but they lack domain-specific knowledge and fail to adhere to domain principles
Solution Approach 1:
The training process is segmented into distinct phases: initial pre-training on general data to acquire language understanding, followed by separate domain-specific fine-tuning phases. This allows the model to maintain general capabilities while adding domain-specific knowledge in a structured manner.
Solution Approach 2:
Domain-specific principles, guidelines, and data are prepared and integrated into the training process before the model is deployed for actual domain tasks. This preliminary alignment ensures the model is pre-equipped with domain knowledge rather than learning it during deployment.
2Ease of operation
If LLMs use reinforcement learning with human feedback (RLHF) to improve alignment, then they generate more helpful content, but they still produce factually incorrect, toxic, or deceiving content
Solution Approach 1:
The system implements multi-stage feedback mechanisms including RLHF for helpfulness alignment, combined with fact-checking feedback loops that verify factual accuracy. Domain experts provide feedback on both helpfulness and accuracy, creating a comprehensive alignment signal.
Solution Approach 2:
Fact-checking modules and domain verification systems act as intermediaries between the model generation and final output. These intermediaries verify factual accuracy independently before presenting results to users, separating the helpfulness function from the accuracy verification function.
3Adaptability or versatility
If foundation LLMs are generically pre-trained, then they can perform broadly across tasks, but they lack knowledge of domain-specific organizational guidelines, standards, rules, intentions or values
Solution Approach 1:
The system merges general language model capabilities with domain-specific knowledge bases by integrating domain principles, guidelines, and standards directly into the model architecture or training data. This combination allows the model to access both general and domain-specific information simultaneously.
Solution Approach 2:
The alignment framework is designed to be universal across different domains by using domain-agnostic architectures that can load and adapt to various domain-specific principles. The same framework can be applied to medical, legal, financial, or other domains by simply changing the domain-specific input data.
4Reliability
If LLMs are trained to follow domain principles through post-training or fine-tuning, then they generate compliant responses, but they require additional training resources and time
Solution Approach 1:
Domain-specific alignment is performed as a preliminary step before deployment, so the model is pre-aligned with domain principles and ready for immediate use. This eliminates the need for time-consuming alignment during actual domain operations.
Solution Approach 2:
The system uses parameter-efficient fine-tuning techniques that modify only specific model parameters related to domain alignment rather than retraining the entire model. This significantly reduces training time and computational resources while maintaining alignment effectiveness.
Data Source
AI summary
An agentic workflow system and method generate question and answer pairs and prompts that may be used to aligns 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. The system and method may also generate aligning processes that may be used to post-train an already trained generative artificial intelligence system or fine tune the training of the generative artificial intelligence system to align that generative artificial intelligence system with the principles of the specific domain. The system and method may be used to align the generative artificial intelligence system to a plurality of different domains.