LLM/LMM Alignment Through Domain-Principle Fine-Tuning
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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, lacking clear understanding of domain-specific guidelines.
Innovation Solution
Post-training and fine-tuning LLMs and LMMs with domain-specific principles using alignment algorithms and instruction generation techniques, incorporating domain-specific training data and expert-crafted instructions to ensure compliance with ethical and regulatory standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs are pre-trained on massive general-purpose corpora, then they achieve broad language understanding and generation capabilities, but they lack domain-specific knowledge and fail to adhere to domain-specific principles
Solution Approach 1:
The patent segments the training process into distinct phases: initial pre-training on general corpora to establish broad language capabilities, followed by separate domain-specific fine-tuning phases. This segmentation allows the model to maintain general versatility while acquiring domain-specific knowledge through targeted training on domain principles, guidelines, and regulations.
Solution Approach 2:
The patent applies preliminary action by pre-loading domain-specific principles, guidelines, and regulations into the model before it encounters actual domain tasks. This preliminary exposure to domain constraints ensures the model has prior knowledge of domain-specific requirements, enabling it to generate compliant outputs from the outset rather than learning through trial and error.
2Object-affected harmful factors
If LLMs use reinforcement learning with human feedback (RLHF) to reduce harmful content, then they improve safety and reduce toxic content, but they still generate factually incorrect and domain-noncompliant content
Solution Approach 1:
The patent introduces domain-specific principles, guidelines, and regulations as intermediary knowledge that mediates between the model's general language capabilities and domain-specific requirements. This intermediary layer of domain knowledge acts as a bridge, ensuring that the model's outputs align with both safety requirements and domain-specific accuracy standards.
Solution Approach 2:
The patent enables the model to self-correct and self-validate by providing it with domain-specific principles and guidelines that it can reference during generation. The model uses these self-provided resources to check its own outputs against domain requirements, reducing reliance on external human feedback while maintaining high domain-specific accuracy.
3Productivity
If foundation LLMs are generically pre-trained, then they can perform well in broad contexts, but they lack knowledge of domain-specific organizational guidelines, standards, rules, intentions or values
Solution Approach 1:
The patent merges general language model capabilities with domain-specific knowledge by combining the pre-trained model with additional domain principles, guidelines, and regulations. This merging creates a hybrid system that retains the efficiency and versatility of the foundation model while incorporating the specialized knowledge needed for domain-specific compliance.
Solution Approach 2:
The patent changes the knowledge parameters of the model by introducing domain-specific principles, guidelines, and regulations as additional training data. This parameter change transforms the model from a general-purpose system to one that is equipped with specific domain knowledge, enabling it to understand and adhere to domain-specific organizational guidelines, standards, rules, and values.
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.