Small Language Model Training with LLM Augmentation
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Solution Overview
Problem
Large language models are impractical to adapt and deploy in specific domains due to their high complexity, leading to excessive resource consumption and limited performance, while small language models lack contextual intelligence and generate inferior outputs.
Innovation Solution
Utilize a large language model for imitation and reinforcement learning to train and augment a small language model, bypassing the need for manually labeled data and human feedback, and leverage the large model's capabilities to enhance the small model's performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large language model is deployed in a specific domain, then high-quality natural language outputs with contextual intelligence are achieved, but resource consumption and training time become excessively high
Solution Approach 1:
The system segments the language model into two distinct components: a large language model (LLM) for generating high-quality training data and a small language model (SLM) for actual domain-specific deployment. This segmentation allows the LLM to be used only during the training phase to create pseudolabeled datasets, while the lightweight SLM handles inference, thereby resolving the contradiction between output quality and resource consumption during deployment.
Solution Approach 2:
The system performs preliminary action by using the LLM to generate pseudolabeled training data in advance before deploying the SLM. The LLM processes unlabeled domain data to create high-quality training examples with natural language labels, which are then used to train the SLM. This preliminary data generation eliminates the need for expensive manual labeling and enables the SLM to achieve high performance without requiring the LLM's computational resources during deployment.
2Adaptability or versatility
If a large language model is adapted to a specific domain, then domain-specific performance is improved, but training time and computational resources become inordinately high
Solution Approach 1:
The system creates a copy of domain knowledge and language patterns by using the LLM to generate pseudolabeled training data that captures domain-specific characteristics. The SLM then learns from these synthesized examples, effectively copying the LLM's domain expertise into a smaller model that can be quickly trained and deployed. This copying approach avoids the need to adapt the entire LLM to the domain, significantly reducing training time while maintaining domain-specific performance.
3Manufacturing precision
If manually labeled training data is used, then training quality is improved, but costs and limitations in data availability increase
Solution Approach 1:
The system implements self-service by enabling the LLM to automatically generate high-quality pseudolabeled training data without human intervention. The LLM processes unlabeled domain data and produces training examples with accurate natural language labels, eliminating the need for expensive and time-consuming manual labeling processes. This self-generated training data maintains high quality while being freely available in unlimited quantities.
4Use of energy by moving object
If a small language model is used, then resource consumption is reduced, but contextual intelligence and output quality deteriorate
Solution Approach 1:
The system changes the parameter of model size by using a small language model instead of a large one for deployment, while compensating for the loss of contextual intelligence through careful training on high-quality pseudolabeled data generated by the LLM. The SLM learns domain-specific patterns and language nuances from the LLM's training examples, enabling it to achieve adequate contextual understanding for its size and application domain, thus resolving the contradiction between resource efficiency and intelligence.
Data Source
AI summary
The techniques described herein enhance the operations of natural language generation systems through training and/or augmentation by a large language model. In a first example, the large language model can execute training operations by processing a training dataset to produce a natural language output. The natural language generation system can analyze the training dataset and the natural language output to generate a natural language output mimicking the output of the large language model. The large language model can then evaluate the output of the natural language generation system to iteratively adjust and improve the quality of natural language outputs. In a second example, the large language can augment a small language model in executing natural language tasks. This is accomplished by retrieving external information using the large language model to generate an augmentation input to provide context and a language framework to the small language model to enhance overall outputs.