GE-PEFT: Gated Expandable Parameter-Efficient Fine-Tuning for Continual Learning
PEFT;Continual Learning;Knowledge Transfer;Language Models
Yes
No ethics review needed.
Questions:
- Why prefix tuning for BERT, AfroXLMR, and T5, and LoRA for Llama? Isn’t prefix tuning known to be hard to optimize, and therefore kind of bad? I would expect all PEFT approaches to be applied to all models, and especially LoRA to be applied to all models since it’s generally known to work better. 
- How do SOTA and/or very basic methods for CL compare?
- Why was the Yelp dataset dropped from the sequence for Llama models? I understand that this was done in prior work, but I’d like to understand why, and whether the same reasoning is appropriate here.
- How does your method perform on longer task sequences, like the 15-task sequence from Wang et al. 2023 and Razdaibiedina et al. 2023?

Notes:
- The introduction is very long and takes a long time to get to the point where you describe your approach. To improve flow, I would recommend moving some of the discussion in the intro to background/related work sections and making the intro more concise so that a reader can more quickly get the gist of the proposed method and its motivation. It’s reasonable to assume that a reader is familiar with continual learning and catastrophic forgetting, for example, only a sentence or two is needed to establish that that is the focus area of the work in the intro. 
- Fix references on lines 300-301, should be e.g. \citep not \citet.
- **Interesting problem setting.** Continual learning, and specifically parameter-efficient continual learning, is an important and realistic problem setting. 
- **Paper is well-written.** The paper was reasonably well-written and easy to understand. 
- **Simple method.** The method is relatively straightforward to implement and does not add too many new hyperparameters; the most finicky part seems to be the process for determining expansion, but the expansion doesn’t seem to be needed in many cases anyway.
In this paper the authors propose a method for parameter-efficient finetuning, GE-PEFT that is specifically designed for the (task-incremental) continual learning setting. It works by combining two components: a gating mechanism that consists of a learned, per-task binary mask over the PEFT weights (to minimize catastrophic forgetting while allowing for parameter sharing in the PEFT weights), and expansion of the PEFT weights (in order to provide sufficient capacity as new tasks are added). They demonstrate the approach on two PEFT methods: LoRA and prefix tuning, on text classification benchmarks: AfriSenti (sentiment analysis on 12 African languages), Web-of-Science (document classification, near domain) and MTL5 (document classification, far domain) benchmarks. They experiment with BERT, T5 and Llama models, in full and few-shot settings, although the full cross product of these experiments is not considered. They show that their proposed method out-performs other CL PEFT methods in terms of final overall micro-averaged accuracy after sequential training.
- **Insufficient comparison to baseline CL methods.** In experimental results, the authors compare to recent state-of-the-art methods specific to parameter-efficient CL, but not state-of-the-art CL methods more broadly, or even old but effective methods such as EWC and episodic replay. As far as I can tell there is no reason that those methods would not also work for PEFT. In the related work section in the appendix, justification is given based on the fact that replay requires keeping examples around, and that both strategies do not *entirely* eliminate catastrophic forgetting. But it would make sense to actually demonstrate that this is the case, especially since the experimental setting in the paper does not involve many tasks (which would mitigate the issue of keeping examples around for each task, for example). However, forgetting is not even evaluated in experimental results. Optimization-based approaches such as MC-SGD [(Mirzadeh et al. 2021)](https://arxiv.org/abs/2010.04495) are not even mentioned. 
- **Unclear and insufficient evaluation metrics.** It guess the numbers reported in the tables are average accuracy across all tasks in the test set after sequential training? It’s not clear whether the authors are micro- or macro-averaging across tasks. It’s not clearly stated, and there are many ways to evaluate aspects of performance in the task-incremental setting beyond average accuracy: you might also measure forgetting (backward transfer), and task accuracy (forward transfer). This way one can get a deeper sense for how different approaches trade off these aspects of CL performance, and better support the claims in the paper. See [Lopez-Alt & Ranzato (2017)](https://proceedings.neurips.cc/paper/2017/hash/f87522788a2be2d171666752f97ddebb-Abstract.html) for further discussion/definitions.
- **Insufficient analysis of results.** There are not compelling experiments or other analysis explaining the experimental results. For example, adding the expansion component only seems to help in a single experimental setting (BERT+AfriSenti), and does not seem to provide much in the other settings. Why is this the case? When should a user bother with that aspect of the model?
- **Experimental setup follows previous work, but is simply not that compelling.** The task-incremental CL setting with a small number of tasks (5-12 in this case, with 12 actually being the same task, but different languages) is a standard CL benchmark, but it’s just not that realistic of a CL setting (versus natural distribution shifts, such as temporal or domain shift.) This limits the real-world applicability of the approach. This work does not experiment with longer diverse task sequences, such as the 15 task sequence benchmark used in baselines from Wang et al. and Razdaibiedina et al. Additionally, very few task orderings are experimented with, even though for some experiments there are only 4-5 tasks and therefore 4! = 24 or 5! = 120 possible task orderings. In those cases, especially since the proposed method is PEFT, evaluating every task ordering seems feasible, or at least more than 3, which seems like it would be needed to obtain statistically significant results. This would improve robustness of experimental results. 
- **No/limited efficiency evaluation.** Despite proposing a parameter-efficient finetuning method, there are no efficiency evaluations in the experimental results. Particularly since the approach adds parameters with respect to each task, how does that additional overhead trade off with accuracy? Aside from mentioning an improvement in number of parameters over a baseline in a single experimental setting, the efficiency compared to baselines is not discussed. Ideally the authors would report latency and/or memory requirements of the proposed approach compared to baselines to assess the practicality of the approach as the number of tasks increases. The parameter analysis experiments start to get at this, but they don’t even include the setting (BERT+AfriSenti) that benefitted most from the parameter expansion, for some reason. Differences in accuracy in these tables is small and could be due to random variation. 
- **Missing related work.** The related work section was relegated to the appendix. In my opinion this is not appropriate and the paper should have been revised in order to properly make room for discussion of related work. Also, a lot of seemingly related work is missing, e.g. [Hyder et al. (2022)](https://arxiv.org/abs/2207.09074), [Wortsman, et al. (2020)](https://arxiv.org/abs/2006.14769), [Mirzadeh et al. (2020)](https://arxiv.org/abs/2010.04495), [Chaudhry et al. (2019)](https://arxiv.org/abs/1812.00420). Just some examples, not an exhaustive list.
Yes
No ethics review needed.
Why does the expandable parameters strategy provide little additional benefit in many cases? Given its complexity and the extra parameters required, its inclusion seems to lack sufficient justification.

I would appreciate it if the authors could address the concerns or clarify if I have misunderstood any part of the paper.
1. The authors effectively utilize the Gated PEFT module in conjunction with an expandable parameters strategy, clearly motivated to address the catastrophic forgetting in continual learning for PEFT.

2. The proposed method can be implemented in both LoRA and prefix tuning.

3. The proposed method outperforms several baseline methods across four different language models.
The paper introduces the Gated Expandable PEFT module, which improves continual learning by effectively addressing knowledge transfer, preventing catastrophic forgetting, and enhancing parameter efficiency and scalability.  This method can be applied to both LoRA and prefix tuning. The authors implement a dynamic adjustment mechanism for model size to align with the needs of current tasks while retaining previously acquired knowledge. Experimental results across various models demonstrate that the proposed method outperforms several baseline methods.
1. The Gated layer method employed has been utilized in previous CL work, while the additional expandable parameters strategy has little effect (as shown in Table 3 and 4). 

2. Tables 5 to 7 are difficult to read and should be presented in figure.

Minor:

3. Using subsections to structure Section 4 would improve its readability.

4. Reporting the standard deviation of results averaged over random seeds would enhance clarity.
Yes
No ethics review needed.
The paper is generally well-written, while the paper needs more explanation about the difference and novel improvements between PEFT and GE-PEFT.
A reasonable continuous learning approach is proposed, which employs dynamically expanding shared parameters and a gating mechanism to solve the problems of parameter efficiency and knowledge migration in continuous learning.
This paper proposes a method called GE-PEFT for solving continuous learning problems. This method addresses four key CL criteria simultaneously by integrating a dynamically expanding PEFT module and a gating mechanism: preventing forgetting, parameter efficiency, knowledge transfer, and managing model capacity. The experimental results show that GE-PEFT performs well in multiple-task incremental CL benchmark tests and is more effective than the best existing methods. In addition, the author provides code and data to promote open research and reproducibility.
1.Although the model design is reasonable, it seems an ensemble of existing methods such as gate mechanism and mask technique. 

2.The difference in performance between G-PEFT and GE-PEFT in the ablation experiment is insignificant, and the effectiveness of the E module has not been verified in the experiment.

3.This paper needs a detailed theoretical analysis of the proposed GE-PEFT methodology. For example, it is questionable whether expandable LoRA can effectively update the parameters in LLMs. 

4.Most comparison methods are PEFT and its variants, it would be better to include more relevant baselines. Besides, it would be better to evaluate each method with the 4 criteria that the proposed method has well addressed as claimed.
Yes
No ethics review needed.
1. Which version of LLaMA-2 does the paper use? The base version or the instruct version?
1. The proposed approach is well-motivated.  
2. The proposed approach is novel.  
3. The proposed approach show good performance.
This paper proposes Gated Expandable Parameter-Efficient Fine-Tuning (GE-PEFT), a framework designed for continual learning in LLMs to address catastrophic forgetting. GE-PEFT utilizes a single, expandable PEFT module with a gating mechanism to memorize task-specific knowledge and dynamically adjusts its size for new tasks. Experiment results across multiple benchmarks show that GE-PEFT shows good performance.
1. It would be better to compare model performance on the benchmark datasets before and after training, so the readers will learn the effect of the learning process on previously learnt task. Currently, the authors just report the performance after training. It would be a plus if the authors can include the model performance before training.   
2. Although the overall approach is novel, some techniques, such as gate, LoRA, prefix tuning are not new.  
3. It would be better to have a case study.
@inproceedings{
anonymous2024gepeft,
title={{GE}-{PEFT}: Gated Expandable Parameter-Efficient Fine-Tuning for Continual Learning},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=NmiFwEP8K5},
note={under review}
}
Continual learning (CL) is a research field focused on continuously adapting foundation models such as large language models (LMs) to newly emerging information sources and tasks. While aspects such as parameter efficiency, knowledge transfer, and managing model capacity have recently received attention, the main research focus in CL remains on preventing catastrophic forgetting. Specifically, there is a lack of solutions that address all these aspects simultaneously. We bridge this gap by introducing Gated Expandable Parameter-Efficient Fine-Tuning (GE-PEFT). Our approach shares knowledge of previous tasks through leveraging a single, dynamically expanding PEFT module within LMs while selectively gating irrelevant previous tasks. Our experiments across multiple task-incremental CL benchmarks demonstrate that GE-PEFT outperforms existing state-of-the-art CL approaches in both full CL and few-shot settings. Our ablation and parameter sensitivity studies highlight the benefit of each proposed component, demonstrating that GE-PEFT offers a more efficient and adaptive solution for CL in LMs.
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PEFT Continual Learning Knowledge Transfer Language Models
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applications to computer vision, audio, language, and other modalities
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GE-PEFT: Gated Expandable Parameter-Efficient Fine-Tuning for Continual Learning
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