Gradient-Based Model Merging for Multi-Domain Language Models
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Solution Overview
Problem
Existing language models lack domain-specific knowledge and require significant resources and time for fine-tuning, leading to inefficiencies and potential performance degradation.
Innovation Solution
A gradient-based model merging technique that identifies and prioritizes domain-sensitive weights from multiple fine-tuned models to create a unified model, reducing the need for extensive re-training and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fine-tuning language models for specific domains is performed, then domain-specific knowledge and performance are improved, but significant time and compute resources are consumed
Solution Approach 1:
Multiple language models are pre-fine-tuned on different domain-specific datasets in advance, so that when domain-specific performance is needed, the pre-trained models can be quickly merged using gradient vectors rather than performing time-consuming fine-tuning from scratch
Solution Approach 2:
Multiple pre-fine-tuned language models are merged into a single unified model by combining their gradient vectors, allowing the model to inherit domain-specific knowledge from multiple sources without retraining, thus reducing fine-tuning time while maintaining performance
2Reliability
If fine-tuning language models for specific domains is performed, then domain-specific knowledge and performance are improved, but significant compute resources are consumed
Solution Approach 1:
Multiple pre-fine-tuned language models are merged into a single unified model by combining their gradient vectors, allowing the model to inherit domain-specific knowledge from multiple sources without retraining, thus reducing compute resource consumption while maintaining performance
Solution Approach 2:
Instead of creating new fine-tuned models for each domain, the system copies and combines the gradient information from multiple existing fine-tuned models to create a unified model that possesses knowledge from all domains
3Adaptability or versatility
If multiple fine-tuned models are created for different domains, then domain-specific expertise is improved, but model complexity and management difficulty increase
Solution Approach 1:
Multiple domain-specific fine-tuned models are merged into a single unified model that encompasses knowledge from all domains, simplifying model management while maintaining adaptability across different domains through gradient vector combination
Data Source
AI summary
Certain aspects of the disclosure provide techniques for model merging. A method may include, for each respective model of multiple models trained for multiple domains: processing a multiple questions to generate multiple predicted answers; for each respective predicted answer: generating a gradient vector indicating, for each weight of the respective model, a weight change that is needed to minimize a loss value, the loss value being based on the respective predicted answer and an incorrect answer to the respective question; and summing each gradient vector for each respective answer to generate a final gradient vector for the respective model; and combining, based on the final gradient vector generated for each respective model, at least one weight of the weight(s) associated with each respective model of the multiple models to obtain a single merged model associated with the plurality of domains.