Parallel Adapter Finetuning for Multi-Task Neural Networks
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Solution Overview
Problem
Sequential finetuning of neural networks for multiple tasks or domains is inefficient and risks erasing previously learned knowledge due to repeated computationally expensive training iterations and overwriting of earlier adaptations.
Innovation Solution
Implement a parallel adapter-based training paradigm where multiple adapter neural networks are trained in parallel on specific tasks or domains, then selectively merged with a base neural network to create a finetuned model, preserving knowledge and reducing computational cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sequential finetuning is used to adapt LLM to multiple tasks, then the model can be adapted to different tasks one after another, but computational cost increases due to repeated training iterations and knowledge from previous datasets is erased
Solution Approach 1:
The patent segments the finetuning process by introducing separate adapter modules for each task or domain. Instead of retraining the entire LLM sequentially on each dataset, the system divides the adaptation into independent adapter components (e.g., adapter1 for legal documents, adapter2 for mathematical problems) that can be trained separately and merged with the base model. This segmentation reduces computational cost by avoiding repeated full-model training while maintaining adaptability to multiple tasks.
2Adaptability or versatility
If sequential finetuning is used to adapt LLM to multiple tasks, then the model can be adapted to different tasks one after another, but training time increases due to repeated computationally expensive training iterations
Solution Approach 1:
The patent applies preliminary action by pre-training a base LLM model once with general knowledge, then using this pre-trained model as the foundation for all subsequent task adaptations. The adapter modules are trained in parallel on different datasets rather than sequentially, which significantly reduces training time. The base model's pre-learned knowledge serves as a starting point that eliminates the need for repeated full-model training iterations for each new task.
3Adaptability or versatility
If sequential finetuning is used to adapt LLM to multiple tasks, then the model can be adapted to different tasks one after another, but previously learned knowledge is erased when updated on new datasets
Solution Approach 1:
The patent extracts task-specific knowledge from the finetuning process by isolating it into separate adapter modules. Each adapter (e.g., adapter1 for legal domain, adapter2 for mathematical domain) captures domain-specific patterns without modifying the base LLM's general knowledge. When adapters are merged with the base model, the base model's pre-learned knowledge is preserved while task-specific adaptations are added on top, preventing the erasure of previously learned information that occurs in sequential finetuning.
Data Source
AI summary
Embodiments described herein provide a parallel adapter-based training paradigm that trains multiple adapters in parallel for specific tasks or domains. The trained adapters are then selectively merged with a base neural network to produce a new finetuned neural network that is finetuned to perform the specific tasks. In this way, the parallel training largely improves computational efficiency to train or adapt a neural network for different tasks without repeated retraining of the entire neural network.