Cross-Model Weight Initialization for Language Model Training
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Solution Overview
Problem
Training multiple generative and representational language models requires significant computational resources and time, leading to performance loss across downstream tasks, with existing solutions either increasing model size or doubling computing resources.
Innovation Solution
Initializing a representational model with the weights of a generative model, and vice versa, to transfer knowledge and reduce training time and computational costs while maintaining performance across tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple generative and representational language models are trained separately, then performance across downstream tasks is improved, but computational resources and training time are significantly increased
Solution Approach 1:
The patent merges the training processes of generative and representational models by using one trained model to initialize the other. Specifically, a representational model is initialized with weights from a trained generative model, or vice versa. This combination approach allows the system to achieve performance benefits of having both model types while reducing the computational burden of training them completely separately.
Solution Approach 2:
The patent applies preliminary action by training one model type first (either generative or representational) and then using its pre-trained weights as initialization for the second model type. This preliminary training establishes a strong foundation that accelerates subsequent training and reduces overall computational resources needed, while still achieving the performance benefits of having both model types.
2Reliability
If model size is increased to reduce performance loss across tasks, then downstream task performance is improved, but training time and computational costs are doubled
Solution Approach 1:
Instead of increasing model size to handle multiple tasks, the patent merges the functionality of separate generative and representational models by initializing one with weights from the other. This approach maintains competitive performance across downstream tasks without requiring larger model architectures, thereby avoiding the increased training time and computational costs associated with scaling model size.
Solution Approach 2:
The patent changes the initialization parameters of the second model by setting its weights to match the pre-trained first model. This parameter transfer allows the second model to start from a knowledgeable state rather than random initialization, significantly reducing training time while maintaining performance quality without needing to increase model size.
3Measurement precision
If two separate models (generative and representational) are trained, then accuracy across tasks is maintained, but computing resources are doubled
Solution Approach 1:
The patent combines the training efficiency of one model with the performance benefits of having both generative and representational capabilities. By initializing the second model with weights from the first, the system maintains accuracy across tasks while using the same computing resources as training a single model, rather than doubling resources to train two completely separate models.
Data Source
AI summary
The present disclosure relates to a system, method and non-transitory computer readable medium for training language models. The exemplary method includes obtaining a first language model. The method includes using a determined set of weights of the first language model to initialize a second language model. The first and second language model are different model types. The method includes applying the second language model to perform an operation.