Adapter Fusion Matrices for Multi-Requirement Model Adaptation
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Solution Overview
Problem
Existing computer-implemented models, such as large language models (LLMs), face challenges in efficiently adapting to various requirements of an entity, leading to increased costs and resource wastage due to individual re-training and maintenance for each task, which negatively impacts computing device performance.
Innovation Solution
The use of adapter tuning, adapter fusion, and model adaptation matrices to group similar requirements into adaptation groups, allowing pre-trained models to be tailored efficiently, reducing the need for multiple adaptations and conserving computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If individual re-training is performed for each task requirement, then model adaptability is improved, but computational resource usage and costs increase
Solution Approach 1:
Multiple task-specific adapter modules are merged into a single unified adapter module through adapter fusion. Instead of training and maintaining separate adapters for each task (sales, marketing, R&D, technical support), the system combines them into one shared adapter that can handle multiple tasks, thereby reducing computational overhead and resource usage while maintaining adaptability across different business functions
Solution Approach 2:
The unified adapter module is designed to serve multiple purposes across different departments and tasks. A single adapter module can be used by sales, marketing, R&D, and technical support teams, eliminating the need for separate specialized adapters and reducing the overall computational burden of maintaining multiple task-specific models
2Reliability
If multiple separate models are trained for different requirements, then task-specific performance is improved, but device complexity increases
Solution Approach 1:
The system merges multiple separate task-specific adapter modules into a single unified adapter module. This consolidation reduces the number of models that need to be trained, stored, and maintained, thereby simplifying the overall system architecture and reducing device complexity while preserving task-specific performance through the fused adapter's ability to handle multiple tasks
3Measurement precision
If individual model maintenance is performed for each task, then model accuracy is improved, but time consumption increases
Solution Approach 1:
The system combines multiple task-specific adapter maintenance operations into a single unified adapter maintenance process. Instead of separately training, evaluating, and updating adapters for each department (sales, marketing, R&D, technical support), the system performs these operations once on the unified adapter, significantly reducing the time required for model maintenance while maintaining accuracy across all tasks through the fused adapter's multi-task capability
Data Source
AI summary
Methods and systems for managing computer-implemented models are disclosed. In particular, existing computer-implemented models (e.g., pre-trained models) may be tailored and adapted to fit the various requirements of an entity using a combination of adapter tuning, adapter fusion, and an adaptation group matrix. Such pre-trained models may be tuned using the adaptation group matrix to obtain one or more adaptation-group-tuned models. The adaptation group matrix may be continuously updated based on changes to the various requirements. Changes to the adaptation group matrix may cause the one or more adaptation-group-tuned models to be updated.