Aligning Large Multimodal Models with Domain Principles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current large multimodal models (LMMs) and large language models (LLMs) lack alignment with domain-specific principles, leading to generation of factually incorrect, toxic, or harmful content, and struggle to perform tasks requiring adherence to specific domain standards, due to pre-training on incomplete or conflicting data, resulting in low-confidence signals and unclear understanding of appropriate behavior.
Innovation Solution
The system and method involve post-training or fine-tuning existing LMMs/LLMs using domain-specific knowledge and principles, employing an instruction-set generation agent to automate the generation of instructions that align the models with specific domain principles, ensuring compliance with ethical and regulatory standards during both training and inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LMMs/LLMs are pre-trained on massive amounts of data to achieve general-purpose language understanding and generation, then the models acquire broad capabilities, but they lack alignment with domain-specific principles and generate factually incorrect or harmful content
Solution Approach 1:
The patent divides the training process into distinct phases: pre-training on general data followed by post-training on domain-specific data. This segmentation allows the model to first acquire broad language capabilities and then specialize in domain-specific principles, resolving the contradiction between general versatility and domain-specific reliability
Solution Approach 2:
The patent applies preliminary action by performing post-training after pre-training. The domain-specific alignment is established as a preliminary step before deployment, ensuring that the model internalizes domain principles upfront rather than attempting to learn them during inference
2Productivity
If LMMs/LLMs are pre-trained on incomplete or conflicting data, then the models can be trained faster with available data, but they produce low-confidence signals and unclear understanding of appropriate behavior
Solution Approach 1:
The patent ensures continuity of useful action by extending the training process from pre-training to post-training without interruption. This continuous training approach allows the model to progressively refine its understanding, maintaining confidence levels by continuously exposing the model to relevant domain data rather than relying on incomplete pre-training data
3Reliability
If complex prompts are used to ensure adherence to domain principles, then the models can generate more accurate content, but the efficiency and cost-effectiveness decrease
Solution Approach 1:
The patent applies preliminary action by embedding domain principles directly into the model during post-training. This preliminary internalization eliminates the need for complex prompts during inference, as the model has already learned to adhere to domain principles automatically, thereby improving efficiency while maintaining reliability
Data Source
AI summary
A system and method 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 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.