LLM Augmentation via Knowledge Language Model Translation Module
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Solution Overview
Problem
Large language models (LLMs) struggle with domain-specific knowledge due to limitations in their training data, leading to computationally expensive and time-consuming fine-tuning processes, especially for very large models.
Innovation Solution
The approach involves disentangling instruction following and domain knowledge acquisition by training a smaller language model on domain-specific knowledge, allowing the pre-trained LLM to remain unchanged, and using a translation module to combine the strengths of both models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fine-tuning the entire LLM on domain-specific data is performed, then domain expertise is improved, but computational cost and training time increase significantly
Solution Approach 1:
The system divides the language model into two separate components: a pre-trained LLM for general language understanding and a smaller domain-specific model for specialized knowledge. This segmentation allows domain expertise to be acquired without retraining the entire LLM, significantly reducing computational cost while maintaining domain reliability.
Solution Approach 2:
A translation module serves as an intermediary between the pre-trained LLM and the domain-specific model. It translates instructions into a format suitable for the smaller domain model, enabling the system to acquire domain expertise efficiently without requiring computational resources to retrain the full LLM.
2Reliability
If fine-tuning the entire LLM on domain-specific data is performed, then domain expertise is improved, but training time increases significantly
Solution Approach 1:
By separating the LLM into a pre-trained component and a smaller domain-specific component, the training time for acquiring domain expertise is dramatically reduced. Only the smaller model needs to be trained on domain data, not the entire LLM, making the process much faster.
Solution Approach 2:
The pre-trained LLM performs preliminary language understanding training before domain-specific training is applied to the smaller model. This preliminary action is completed once during pre-training, allowing subsequent domain training to proceed quickly without repeating the entire pre-training process.
3Adaptability or versatility
If the LLM is retrained to accommodate new domains, then adaptability is improved, but computational cost and inflexibility increase
Solution Approach 1:
The system segments the model into a stable pre-trained LLM and a flexible domain-specific model. When adapting to new domains, only the smaller model is retrained, not the entire LLM. This reduces the complexity of the retraining process while maintaining high adaptability to new domains.
Solution Approach 2:
The architecture allows dynamic adaptation to new domains by training only the smaller domain-specific model while keeping the pre-trained LLM unchanged. This dynamic approach enables the system to adapt to new domains efficiently without the complexity of retraining the entire large model.
4Use of energy by moving object
If a smaller language model is trained on domain-specific knowledge, then computational cost is reduced, but instruction following capability may be compromised
Solution Approach 1:
The translation module acts as an intermediary that bridges the pre-trained LLM and the smaller domain-specific model. It translates instructions into a format that the smaller model can effectively process, ensuring that instruction following capability is maintained while using computational resources efficiently.
Solution Approach 2:
The system merges the strengths of both models by combining the pre-trained LLM's general language understanding with the smaller domain-specific model's specialized knowledge and efficient computation. The translation module facilitates this merging, ensuring both instruction following and domain expertise are achieved with reduced computational cost.
Data Source
AI summary
In an example, a method for generating responses by a Machine Learning (ML) system includes processing, by a first language model, a natural language instruction to generate an instruction representation based on a meaning of the natural language instruction; translating, by a translation module comprising an interface between the first language model and a second language model, the instruction representation into data indicating an intent of the natural language instruction, wherein the second language model is trained with domain specific knowledge; providing, by the translation module, the natural language instruction and the data indicating the intent of the natural language instruction to the second language model; and generating, by the second language model, a response based on the natural language instruction and the data indicating the intent of the natural language instruction.