Agentic Workflow for Domain-Aligned AI Response Adjustment
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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 missing information, conflicting information, and unclear understanding of domain principles, necessitating alignment with domain-specific guidelines.
Innovation Solution
A framework is provided to post-train or fine-tune LLMs and LMMs using domain-specific principles, incorporating a training data messages-set generating agent to align models with domain-specific knowledge, ensuring compliance with ethical and regulatory standards through pre-training and post-inference alignment processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If general-purpose LLMs are used, then broad language understanding capability is improved, but domain-specific accuracy and adherence to domain principles deteriorates
Solution Approach 1:
The system segments the alignment process into multiple specialized agents (SFT agent, DPO agent, evaluation agent) that each handle specific aspects of domain alignment, allowing the general LLM to be adapted to specific domains without losing its broad language understanding capabilities
Solution Approach 2:
The system changes the parameters of the LLM through supervised fine-tuning and direct preference optimization, adjusting the model's behavior and knowledge to match domain-specific principles while maintaining its general language capabilities
2Reliability
If domain-specific fine-tuning is applied, then domain adherence is improved, but training time and computational resources increase
Solution Approach 1:
The system performs preliminary supervised fine-tuning to establish basic domain knowledge before applying direct preference optimization, which accelerates convergence and reduces the overall training time compared to starting from scratch
Solution Approach 2:
The evaluation agent provides continuous feedback during the alignment process, allowing the system to iteratively improve domain adherence while monitoring training progress and adjusting parameters to optimize the balance between reliability and training efficiency
3Object-affected harmful factors
If comprehensive domain principles are enforced, then content safety is improved, but model flexibility and creativity deteriorate
Solution Approach 1:
The system applies domain principles locally to specific contexts through the DPO agent, which learns to enforce safety guidelines only when and where they are relevant, preserving the model's flexibility and creativity in areas not constrained by domain principles
Solution Approach 2:
The alignment process is dynamic and adaptive, allowing the model to flexibly apply domain principles based on the specific input context, maintaining creativity while ensuring safety through context-aware enforcement rather than rigid constraints
Data Source
AI summary
An agentic workflow system and method generate question and answer pairs to post inference adjust a generative artificial intelligence (a large language model (LLM) or a large multimodal model (LMM)) response to align the response 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.