Router-Guided Knowledge Infusion for Domain-Specific LLMs Without Retraining
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Solution Overview
Problem
Large language models (LLMs) face performance gaps in knowledge-intensive scenarios due to knowledge gaps, despite their adaptability in natural language processing tasks, which can be exacerbated by computationally expensive integration of domain-specific knowledge during pre-training or fine-tuning.
Innovation Solution
A parameter-efficient adapter-based framework integrates domain-specific knowledge from knowledge graphs using router-guided knowledge infusion, selectively adding information through parallel adapters to enhance LLMs without compromising existing knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If domain-specific knowledge is integrated during pre-training or fine-tuning, then LLM performance in knowledge-intensive tasks is improved, but computational cost increases significantly
Solution Approach 1:
The patent segments the knowledge integration process into two parts: (1) pre-computing domain-specific knowledge representations using knowledge graphs, and (2) efficiently injecting these pre-computed representations into the LLM during inference through adapter layers. This segmentation avoids the computationally expensive process of re-training the entire LLM on domain-specific data while still achieving improved performance in knowledge-intensive tasks.
Solution Approach 2:
The patent performs preliminary action by pre-computing domain-specific knowledge representations from knowledge graphs before they are needed during inference. These pre-computed representations are then injected into the LLM through adapter layers, avoiding the need to perform expensive knowledge integration computations at inference time and significantly reducing overall computational cost.
2Reliability
If adapter layers are added to infuse domain-specific knowledge, then LLM knowledge base is enriched without retraining, but model architecture complexity increases
Solution Approach 1:
The patent implements the nested doll principle by embedding adapter layers within the existing LLM architecture. The adapter layers are integrated into the transformer structure at specific positions, allowing domain-specific knowledge to be infused without replacing the core LLM parameters. This nested integration enriches the knowledge base while maintaining the original model's structure and adding minimal architectural complexity.
Solution Approach 2:
The patent applies local quality by introducing adapter layers only at specific positions within the LLM architecture where domain-specific knowledge injection is most beneficial, rather than uniformly modifying the entire model. This selective integration enriches the knowledge base in targeted areas while minimizing the increase in overall model architecture complexity.
3Productivity
If router-guided knowledge infusion is implemented, then knowledge integration becomes more selective and efficient, but system complexity increases
Solution Approach 1:
The patent implements dynamics by introducing a router mechanism that dynamically determines whether to activate adapter layers for knowledge infusion based on the input query. The router analyzes the query and selectively engages the knowledge injection path only when necessary, making the system adaptive and efficient. This dynamic control improves knowledge integration efficiency while managing system complexity through intelligent activation rather than continuous operation.
Solution Approach 2:
The patent uses a router as an intermediary component that mediates between the input query and the adapter layers. The router evaluates the query and decides whether to route through the adapter layers for knowledge infusion or process the query directly using the base LLM. This intermediary mechanism enables selective and efficient knowledge integration while containing system complexity within a well-defined routing decision structure.
Data Source
AI summary
Methods and systems include determining that a query is relevant to information that is unknown to a pre-trained language model. Outputs from adapter layers are added to outputs of respective transformer layers of the language model to infuse the language model with the information, such that the language model generates a response to the query that accounts for the information that is unknown to the pre-trained language model. An action is performed based on the response.