Factor-Tuning Hidden Representations for Low-Cost Model Adaptation
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Solution Overview
Problem
The growing size of pre-trained language models makes full fine-tuning prohibitively expensive or impossible for downstream tasks, requiring separate replicas for each task and hindering deployment and maintenance.
Innovation Solution
A parameter-efficient fine-tuning mechanism, called Factor-Tuning, learns a compact representation of parameter changes across multiple domains and automatically determines which parameters to update and how to update them efficiently for new downstream tasks using domain-dependent hidden factors and sparse components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full fine-tuning is applied to pre-trained language models for downstream tasks, then task performance is improved, but computational cost and resource requirements become prohibitively expensive
Solution Approach 1:
The patent extracts and updates only the most critical parameters (output embeddings and attention biases) from the full model, rather than updating all parameters. This selective extraction approach maintains task performance while dramatically reducing computational cost and resource requirements during fine-tuning.
Solution Approach 2:
The patent applies different update strategies to different parts of the model: output embeddings and attention biases are updated to adapt to downstream tasks, while the core pre-trained parameters remain frozen. This local quality approach ensures that only the necessary parts of the model are modified, reducing overall computational cost while maintaining performance.
2Reliability
If separate model replicas are created for each downstream task, then task-specific performance is improved, but model deployment and maintenance become increasingly complex
Solution Approach 1:
The patent creates a universal base model that can be efficiently adapted to multiple downstream tasks by updating only a small subset of parameters. This single universal model replaces the need for multiple separate replicas, as the same model architecture can serve different tasks by selectively updating its parameters, thereby reducing deployment and maintenance complexity.
Solution Approach 2:
The patent segments the model parameters into two distinct groups: frozen pre-trained parameters and updateable parameters (output embeddings and attention biases). This segmentation allows the model to be efficiently deployed across multiple tasks by only modifying the small updateable subset, simplifying the management and deployment process compared to maintaining separate full-model replicas.
3Measurement precision
If all model parameters are updated for each downstream task, then adaptation accuracy is improved, but training time and resource consumption increase significantly
Solution Approach 1:
The patent extracts and updates only the most critical parameters (output embeddings and attention biases) that have the greatest impact on downstream task performance. By focusing computational resources on these key parameters rather than all parameters, the method achieves effective adaptation with significantly reduced training time and resource consumption.
Solution Approach 2:
The patent applies partial action by updating only a small subset of parameters (output embeddings and attention biases) rather than all parameters. This partial update strategy is sufficient to achieve good adaptation accuracy for downstream tasks while dramatically reducing the computational burden and training time required.
Data Source
AI summary
Embodiments described herein provide a parameter-efficient finetuning mechanism, referred to as “factor-tuning,” which first learns a compact representation of parameter changes with existing datasets on multiple domains, and then fine-tunes a small number of parameters (automatically extracted from the learned representation) on a new downstream task. In this way, the representation learned in the first step is shared across domains and transferred to new downstream tasks.