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Parameter-Efficient Continual Fine-Tuning: A Survey
Created by
Haebom
Author
Eric Nuertey Coleman, Luigi Quarantiello, Ziyue Liu, Qinwen Yang, Samrat Mukherjee, Julio Hurtado, Vincenzo Lomonaco
Outline
This paper focuses on overcoming the iid assumption of large-scale pre-trained networks and presenting an efficient method for adapting to continuous learning environments. Specifically, we highlight the synergy between continuous learning (CL) and parameter-efficient fine-tuning (PEFT). We survey recent research trends in parameter-efficient continuous fine-tuning (PECFT) and explore various approaches, evaluation metrics, and future research directions. Our goal is to address the catastrophic forgetting problem encountered by existing PEFT methods and to propose a method for continuously adapting large-scale models to diverse tasks.
Takeaways, Limitations
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Takeaways:
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We present a comprehensive overview of the latest research trends in the field of PECFT, which combines continuous learning (CL) and parameter-efficient fine-tuning (PEFT).
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It provides guidelines for researchers by suggesting various PECFT approaches, evaluation metrics, and future research directions.
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It presents a new research direction for solving the problem of continuous learning of large-scale models.
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Limitations:
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Since this paper is a survey of the PECFT field, it does not present new algorithms or experimental results.
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Comparative analysis of the pros and cons of various PECFT methods may be lacking.
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There may be a lack of specifics regarding future research directions.
