Meta-Knowledge Fine Tuning for Cross-Domain Language Model Generalization
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Solution Overview
Problem
Existing fine tuning methods for downstream task language models are limited to specific data sets, restricting the effectiveness of model compression and generalization across different domains in the smart city field.
Innovation Solution
A meta-knowledge fine tuning method using cross-domain typical score learning, which calculates prototypes and typical scores to enhance the fine tuning of multi-task language models, allowing for interrelation and mutual reinforcement across tasks and domains, thereby improving parameter initialization and generalization abilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fine tuning is performed on a specific data set for downstream tasks, then the model achieves good performance on that specific data set, but the model's generalization ability to other domains is limited
Solution Approach 1:
The patent pre-calculates prototypes for each domain before fine-tuning. These prototypes serve as prior knowledge that guides the fine-tuning process across multiple domains. By performing this preliminary action of prototype calculation, the model is prepared to generalize better to unseen domains while maintaining performance on seen domains.
Solution Approach 2:
The patent introduces prototypes as intermediary representations that mediate between different domains. These prototypes act as a bridge that allows knowledge transfer across domains. The typical score calculation uses these prototypes as intermediaries to measure how representative each sample is, enabling the model to generalize across domains while maintaining specificity where needed.
2Reliability
If a large-scale pre-training language model is used, then the model has strong language understanding and generation capabilities, but the model requires significant computational resources for deployment
Solution Approach 1:
The patent extracts and compresses the essential knowledge from large-scale pre-training models into compact prototype representations. By taking out only the critical domain-specific patterns and storing them as prototypes, the system maintains the beneficial capabilities of large models while reducing the computational burden for deployment. This extraction allows the model to function effectively with reduced resources.
Solution Approach 2:
The patent changes the parameter representation from full model weights to compact prototype vectors. This parameter transformation significantly reduces the computational complexity and memory requirements while preserving the essential language understanding capabilities. The typical score calculation uses these changed parameters to maintain performance without requiring the full computational power of the original large-scale model.
Data Source
AI summary
Disclosed is a meta-knowledge fine tuning method and platform for a multi-task language model. The method is to obtain highly transferable shared knowledge, that is, meta-knowledge, on different data sets of tasks of the same category, perform interrelation and mutual reinforcement on the learning processes of the tasks of the same category that correspond to different data sets and are in different domains, so as to improve the fine tuning effect of downstream tasks of the same category on data sets of different domains in the application of the language model, and improve the parameter initialization ability and the generalization ability of a general language model for the tasks of the same category.