Paper: HiddenKey: Parameter-Efficient FineTuning Meets Dropout under a Unified Framework (ICLR-2024)

Reviewer comment (Novelty):
incremental improvement: In Table 1, the HiddenKey method with KL achieves the best score of 92.01 on average, but this is not a significant improvement (< 1%) from either DropKey or HiddenCut methods. Similarly, HiddenKey only demonstrated marginal improvements in Table 2 to 5, with the highest improvement in Table 2 CoLA (1.95%). I would not claim performance superiority given these results.

Author response:
Considering that GLUE is a mature benchmark where the adoption of various techniques has established very strong baselines and left little space for further improvement, the consistent improvements over such strong baselines on both NLU and NLG tasks are still significant. Especially, HiddenKey improves the BLUE by 1.26 and NIST from 8.615 to 8.765 on the E2E dataset. Besides, we conduct repeated experiments to ensure robust conclusions and insights.

Reviewer comment (Experiments):
lack of analysis: While the paper describes the intuition of how HiddenKey differs from DropKey, DropAttention, HiddenCut, it does not give any analysis to describe the difference in empirical experiments e.g. what features were learned or how the computations are empirically different. Minimally, an ablation study is necessary to elucidate which component of HiddenKey is required for the marginal performance gains.

Author response:
We analyze the integration of HiddenKey step by step, of which the process can be regarded as the ablation study. Hence, each component of HiddenKey has been verified to be useful.  Besides, we provide the formulation and comparison of various methods to demonstrate the computation.

Reviewer comment (Evaluation):
unsupported claim for faster convergence: The authors claimed that "HiddenKey outperforms baseline with shorter finetuning process". When training a model, the training loss is usually used as the metric to determine when model training should be stopped. In Fig.5, at the point of the black vertical line, the baseline model has clearly plateaued while HiddenKey is still decreasing. To reiterate, if I were to use the HiddenKey method, I would take reference from the plateauing of the training loss and not the accuracy, which in this case is significantly longer than the baseline method.

Author response:
Essentially, HiddenKey provides an option for users. If resources are constrained, HiddenKey can provide better performance with the same iteration number. As you said, if the longer finetuning process is allowed, HiddenKey can improve the performance further. In both cases, HiddenKey provides extra benefits.

Reviewer comment (Evaluation):
Fig 4 seems that DropKey method is more stable and contributes to a better performance than hidden method with increasing dropout rate?

Author response:
In Figure 4, DropKey does perform better and more stably than HiddenCut. As shown in Table 1, HiddenKey, combining HiddenCut, DropKey, and KL loss, provides the best performance.

Reviewer comment (Evaluation):
Is Fig 5 indicating training or test loss? A decrease in training loss does not indicate overfitting, as hypothesized by the authors.

Author response:
The dashed and solid lines mean the training loss and test acc, respectively. The decreased acc with lower training loss in one trail means the occurrence of overfitting, while lower training loss does not mean better acc for different trails shown in Figure 5.

Reviewer comment (Presentation):
The Introduction conclusively describes the phenomenon of overfitting without dropout in PEFT, leading the reader to associate the paper with “Dropout neurons in LoRA” or something similar. However, in Sections 2 and 3, when formulating problems and methodology, there is nothing about PEFT, which creates confusion about the actual focus of the paper.

Author response:
We will refine our introduction part to make it more logical and clear. Our paper focuses on the empirical analysis of the effect of various transformer-specific Dropout methods on the PEFT performance. With this purpose, the main contribution is to create a framework to unify the design of different Dropout methods and compare their effectiveness in the context of PEFT. We conduct comprehensive experiments to evaluate the performance of nine different Dropout patterns and empirically conclude the best combination - HiddenKey, which is a recommended practice to alleviate the overfitting in the context of PEFT. Besides, we derive some inspiring insights and mathematical co-relation (the similarity in gradient flow of DropKey and DropAttn.) between different Dropout methods.

Reviewer comment (Presentation):
The contribution of the paper is not summarized, which leads to further confusion.

Author response:
Please refer to the first point for our detailed contribution.

Reviewer comment (Experiments):
When discussing dropout methods, the paper is limited to only DropKey, DropAttn, and HiddenCut methods. At a minimum, the basic Dropout or DropConnect should also be investigated.

Author response:
For the selection of Dropout Methods, we mainly focus on those specially designed for Transformer architecture. There are so many methods actually. As for the vanilla dropout, element-wise methods in our paper can be regarded as the basic Dropout, which will be explicitly mentioned in our paper.

Reviewer comment (Theory):
In discussing prevention of overfitting, some analysis of the generalization error bound is expected. However, the paper only conducts gradient calculations to present “proofs” about reducing gradient noise. It is unclear what is meant by gradient noise, and there is no formal definition or theoretical support provided for the claim of noise reduction or lower error bounds.

Author response:
Since the Method section focuses on comparing and unifying different dropout methods, we mathematically analyze the relationship between DropKey and DropAttn. For one iteration, it is hard to gain the clean and noisy gradient simultaneously, disentangle the clean and noisy part due to the complicated back-propagation, and determine the impact on performance. However, we can use detach() to stop the gradient noise to verify its existence.

Reviewer comment (Evaluation):
There's missing analysis of training cost of HiddenKey. Especially, it uses double forward pass with KL and JS training, there must be memory consumption and iteration time implications.

Author response:
Based on our experience, the introduction of KL loss does not significantly increase the training cost. There are two reasons. One is that the second branch does not need backward, which is much slower than forward. The other is that the second branch runs in the torch.no_grad() mode, which does not store the immediate states and thus is much faster than general forward. Besides, two branches can be computed simultaneously, and the increased training time could be ignorable. We will conduct comprehensive research and evaluate it to demonstrate the additional cost given by the KL loss, if any.

Reviewer comment (Writing):
Overall, writing can be improved with more revisions. Even though the sentences are grammatically correct, some sentences look not natural. (e.g. the very first sentence in the abstract - if you could ask chatgpt like tools, it would tell you the sentence is not natural) Some more details are will be in the Questions section.

Author response:
We modify our introduction to make the logic flow more natural.

Reviewer comment (Writing):
The sentence at the end of page 1 (starting with However) and the next sentence are not naturally flow.

Author response:
We modify our introduction to make the logic flow more natural.

Reviewer comment (Writing):
At the end of introduction, DropKey, DropAttention and HiddenCut suddenly appear without any context. Please add some more explanation that those are from previous research and references for them.

Author response:
References of DropKey, DropAttention, and HiddenCut will be added in the introduction section.

Reviewer comment (Evaluation):
Is there a training cost analysis with KL divergence?

Author response:
Based on our experience, the introduction of KL loss does not significantly increase the training cost. There are two reasons. One is that the second branch does not need backward, which is much slower than forward. The other is that the second branch runs in the torch.no_grad() mode, which does not store the immediate states and thus is much faster than general forward. Besides, two branches can be computed simultaneously, and the increased training time could be ignorable. We will conduct comprehensive research and evaluate it to demonstrate the additional cost given by the KL loss, if any.

Reviewer comment (Presentation):
In Figure 4, dropkey_column is HiddenKey? It will be useful if this is mentioned somewhere.

Author response:
DropKey_column is the column-wise DropKey. HiddenKey is not included in the Figure 4. The figure is demonstrated for the illustration of the superiority of element-wise HiddenCut and Column-wise DropKey, indicating the rationale behind our proposed HiddenKey → combining the element-wise HiddenCut, the Column-wise DropKey, and KL loss to maximize the performance.

Reviewer comment (Presentation):
In Table 1, superscripts in HiddenKey+KL seem strange? 1000.00, 5.00. And, why only HiddenKey- has two values? Shouldn't all HiddenKey variants have two values?

Author response:
For HiddenKey-, two values of superscripts mean the dropout rates of element-wise HiddenCut and column-wise DropKey, respectively. For other variants, one value means the weight of KL loss for the optimal performance, and the dropout rates remain the same with HiddenKey-. As for the reason of 1000 (apparently larger than other values), this is caused by the STS-B dataset, which is a regression dataset, and the scale of KL loss is different from other classification tasks.

Reviewer comment (Evaluation):
In Figure 5, can you include the validation loss? It might behave similarly to the accuracy.

Author response:
Sure, we will include it for your reference.

Reviewer comment (Novelty):
The technical novelty for the proposed new method is limited. HiddenKey is an integration of existing dropout methods.

Author response:
Our paper focuses on the empirical analysis of the effect of various transformer-specific Dropout methods on the PEFT performance. With this purpose, the main contribution is to create a framework to unify the design of different Dropout methods and compare their effectiveness in the context of PEFT. We conduct comprehensive experiments to evaluate the performance of nine different Dropout patterns and empirically conclude the best combination - HiddenKey, which is the analysis result instead of the only contribution. Besides, we derive some inspiring insights and mathematical co-relation (the similarity in gradient flow of DropKey and DropAttn.) among different Dropout methods.

Reviewer comment (Evaluation):
In Table 1, it is hard to evaluate different patterns based on the results. The values are close and have large variance. This makes the design choice of combining "elementwise HiddenCut" and "column-wise DropKey" a bit arbitrary.

Author response:
The relatively large variance of our results is subjected to the task configuration (i.e. The performance of LoRA is sensitive to the initialization). To remedy this large variance, we repeat each NLU task five times and take the median value to reduce the randomness, given that the median value is more robust to outliers than the average value. In Table 1, we additionally show the variance for the necessity of repeated trials and readers’ reference. Compared with other published works (e.g., LoRA), our variance falls into a reasonable area. Figure 4. provides a visual illustration of our results, and the superiority of element-wise Hiddencut and column-wise DropKey over different task configurations is demonstrated better.

Reviewer comment (Experiments):
In both NLU and NLG experiments, HiddenKey$^-$ does not exhibit a clear gain over the best baselines, and the improvement of HiddenKey is not very significant. It would be nice to include results on more NLG tasks like wikitext and cnn/dm to demonstrate.

Author response:
The gain given by HiddenKey- is minor before applying KL loss, but it still showcases a positive signal that the average/median performance increases. After applying the KL loss, the superiority of HiddenKey could be confirmed. Another factor contributing to the minor improvements is that the GLUE benchmark is widely adopted, and it is hard to gain large improvement on a strong baseline.

Reviewer comment (Experiments):
(Minor) All claims regarding PEFT methods are only verified on LoRA.

Author response:
Among all PEFT methods, LoRA is one of the most popular methods widely studied in academia and industry. There is also some work to unify different PEFT methods, like “Towards a Unified View of Parameter-Efficient Transfer Learning.” We hope our analysis is also valid for other specific PEFT methods.