Shared-Layer LLM Adapters for Concurrent Task Training
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Solution Overview
Problem
Existing large language models (LLMs) face inefficiencies in adapting to specialized tasks within multi-agent systems due to computational resource constraints, operational costs, and the need for continual retraining, which hinders their practical deployment and scalability.
Innovation Solution
A method and system for adapting large language models (LLMs) by extracting and initializing shared layers from a pretrained model for multiple adapters, creating task-specific models, and training them concurrently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If repeated fine-tuning is performed for every individual task, then task-specific adaptability is improved, but training time and operational cost increase
Solution Approach 1:
The LLM is segmented into multiple layers, and adapters are applied to specific target layers rather than the entire model. This allows selective adaptation of only certain layers for each task, reducing the computational burden and training time while maintaining task-specific performance.
Solution Approach 2:
A single pretrained LLM serves as a universal base model that can be adapted to multiple different tasks through the addition of different adapters. The shared layers provide common capabilities while task-specific adapters provide specialized functionality, enabling one model to perform many functions.
2Adaptability or versatility
If repeated fine-tuning is performed for every individual task, then task-specific adaptability is improved, but operational cost increases
Solution Approach 1:
Multiple adapters for different tasks are merged into a single pretrained LLM architecture, sharing the computation of the base model layers. This consolidation allows the system to perform multiple tasks without repeatedly loading and processing the entire model, reducing operational costs and energy consumption.
Solution Approach 2:
Instead of fine-tuning the entire model for each task, only partial components (adapters for specific layers) are trained and updated. This partial action approach reduces the computational resources and operational costs required while still achieving task-specific adaptability.
3Reliability
If the entire LLM is fine-tuned for each task, then model performance is improved, but computational resource constraints are exceeded
Solution Approach 1:
The model is divided into static base layers and dynamic adapter layers. Only the adapter layers are fine-tuned for each task while the base layers remain frozen, significantly reducing the number of parameters that need to be trained and the computational resources required.
Solution Approach 2:
The approach changes the training parameters by freezing most of the model parameters and only updating the adapter parameters. This parameter change strategy maintains model performance while reducing computational complexity and resource requirements.
Data Source
AI summary
A method and a system of adapting large language model for specific tasks is disclosed. A processor receives a pretrained LLM, training dataset for each of a plurality of adapters, and a set of target layers. A set of layers are extracted from the pretrained LLM based on the set of target layers. The set of layers are initialized as a set of shared layers for each of the plurality of adapters. A plurality of task specific models is created based on the plurality of adapters and the set of shared layers. Each of the plurality of task specific models are trained with a corresponding training dataset, concurrently.