SEFE: Superficial and Essential Forgetting Eliminator for Multimodal Continual Instruction Tuning

Jinpeng Chen, Runmin Cong, Yuzhi Zhao, Hongzheng Yang, Guangneng Hu, Horace Ho Shing Ip, Sam Kwong·May 05, 2025

Summary

SEFE addresses forgetting in Multimodal Continual Instruction Tuning, distinguishing between superficial and essential types. It uses Answer Style Diversification and RegLoRA for parameter stability, showing superior performance in maintaining competencies.

Introduction
Background
Overview of continual instruction tuning in multimodal settings
Challenges in maintaining competencies over time
Objective
To introduce SEFE, a novel approach for addressing forgetting in continual instruction tuning
To differentiate between superficial and essential forgetting types
Method
Data Collection
Description of the dataset used for continual instruction tuning
Importance of multimodal data in the context of SEFE
Data Preprocessing
Techniques for preparing the data for SEFE
Role of preprocessing in enhancing the effectiveness of Answer Style Diversification and RegLoRA
Answer Style Diversification
Explanation of Answer Style Diversification
How it contributes to parameter stability in SEFE
RegLoRA
Description of RegLoRA and its role in SEFE
How it ensures robustness against forgetting
Results
Competency Maintenance
Evaluation metrics for assessing competency maintenance
Comparison of SEFE with existing methods
Superficial vs. Essential Forgetting
Analysis of how SEFE distinguishes and addresses both types
Conclusion
Summary of SEFE's contributions
Future directions and potential applications
Impact on the field of multimodal continual instruction tuning
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
How does RegLoRA contribute to SEFE's performance in maintaining competencies across different modalities?
How does SEFE differentiate between superficial and essential forgetting in Multimodal Continual Instruction Tuning?
What role does Answer Style Diversification play in SEFE's approach to maintaining parameter stability?