From: ICLR.cc/2023/Conference/Paper5395/Authors
title: Submission Withdrawn by the Authors

From: ICLR.cc/2023/Conference/Paper5395/Authors
title: Looking forward to the discussion!
comment: Dear reviewers,

We did several additional experiments and revised our manuscript based on your valuable and constructive reviews. We indeed appreciate it if you would check our responses and re-evaluate our paper.

We are looking forward to discussing with you any further questions or suggestion you might have!

Again, thank you for your time and effort in reviewing our paper!

Best,

Authors of paper 5395

From: ICLR.cc/2023/Conference/Paper5395/Authors
title: Looking forward to the discussion! 
comment: Dear reviewers,

We did several additional experiments and revised our manuscript based on your valuable and constructive reviews. We indeed appreciate it if you would check our responses and re-evaluate our paper.

We are looking forward to discussing with you any further questions or suggestion you might have!

Again, thank you for your time and effort in reviewing our paper!

Best,

Authors of paper 5395

From: ICLR.cc/2023/Conference/Paper5395/Authors
title: Revision summary
comment: Dear AC and Reviewers,

We thank all the reviewers for their thoughtful comments and suggestions. We conducted several additional experiments and revised the manuscript to address your questions and improve our paper. They are briefly summarized as follows:
* We added a column to Table 2 and Table 3 for the pretrained model, which was previously merged with the prompt-tuning column. Hopefully this new presentation is more clear. 
* We added experiments on T5 XXL and FLAN-T5 XXL to show the effectiveness of our method.
* We added experiments for multi-task ProMoT training and compared with standard multi-task training, which shows that multitask ProMoT generalizes better than standard multitask fine-tuning on most unseen tasks.
* We added a new figure to compare the gradient alignments on step 0, 100, 200, 300 and 400 in Figure 8.

We look forward to hearing from you and stand ready to answer your questions and address your concerns. Thank you!

Paper 5395 Authors

From: ICLR.cc/2023/Conference/Paper5395/Authors
title: Response to Reviewer 7DnR
comment: We thank the reviewer for their thoughtful comments, questions and suggestions.
> First of all, I am interested in the paper, as it demonstrates what happens in fine-tuning LMs (though only a small step). The designed method is neat and supported by the empirical studies.

We thank the reviewer for their appreciation of the value of our work. Our discoveries are indeed a small step in understanding the finetuning process of LMs. However we believe that with such advanced understanding, it will become possible to improve a specific general capability of LMs beyond what is possible with pretraining along. For example, can we use a small but high quality NLI dataset to vastly improve the general reasoning ability of a LM, which is a key component of an infinite set of in-context learning tasks? We believe that our work offers hope and important clues towards this direction. 

> The experimental results also confused me sometimes, because it seems that the performance of the models is a little bit random on the datasets. For example, in Table 3, why the Standard Fine-tuning outperforms ProMoT and Prompt-tuning on WiC? On the similar classification task CB, it is the worst one. Similarly, in Table 2, why Prompt-tuning is significantly better than ProMoT on MT tasks? I guess when comparing Prompt-tuning and ProMoT on MT, they are almost equivalent, right?

We would like to emphasize that the zeroth order message of our results is that ProMoT has much less forgetting compared to standard finetuning. This is true for most tasks across the board. 
Compared to the pretrained model, we do observe an interesting collection of changes. We believe that such changes are not random and reflect important semantic relations between different tasks. Such relations are not well explored in the literature. For example, [1] studies the transfer between different styles of QA tasks and [2] studies the transfer between QA, classification and sequence labeling tasks. However, they characterize task relations using the transfer ability using standard finetuning. As demonstrated in this work, this is not the best approach when the tasks have very different format, where standard finetuning does not transfer. [3] which finds that in some cases, higher performance on upstream tasks doesn’t improve performance on down-stream tasks when they do not have much shared information.
Intuitively, we believe that the condition for transfer to happen in standard finetuning is that the tasks share both the format and their semantic content. The condition for transfer to happen in ProMoT is simply their shared semantic content, while the format becomes less important. We observed several robust phenomena that support this intuition. For example there is a robust transfer from RTE to summarization tasks, and from English-French to English-Romania and English-Germany using ProMoT, but not using standard finetuning. While there seems to be some conflict between translation tasks and summarization tasks, which makes sense because summarization involves semantic compression which is not the case for translation. 
In terms of the transfer from translation to CB vs WiC, we first note that CB is quite different from WiC semantically even when they are both binary classification. For WiC there is almost no transfer from translation no matter what method is used as the performances are all very close to chance. This is not surprising as we do not expect translation to have any connection with word sense disambiguation. 
In table 2, we observe that there is some forgetting on translation when we finetune on RTE, even with ProMoT. Note that the prompt tuning performance is the same as the pretraining model as prompt tuning does not change model parameters. We first note that such forgetting is indeed less severe compared to standard finetuning because the task specific format information is offloaded. We also believe that on unrelated tasks such as RTE vs translation, it is likely that ProMoT still has mild forgetting because ProMoT changes the pretrained model during stage 2 while translation cannot benefit from it.

[1] https://arxiv.org/pdf/2005.00700.pdf

[2]https://aclanthology.org/2020.emnlp-main.635.pdf

[3] https://arxiv.org/pdf/2110.02095.pdf

From: ICLR.cc/2023/Conference/Paper5395/Authors
title: Response to Reviewer 7Esg (3/3)
comment: > Given ProMoT is proposed to preserve the ICL ability of a PLM, a PLM with strong in-context learning performance is desired to be used for experiments. However, authors choose to use mT5, which has poor or even chance in-context learning performance.

We chose mT5 because there are several translation tasks. To address this question, we did two additional sets of experiments on the RTE dataset with T5 and FLAN-T5 XXL. T5 has better few-shot performance on QA tasks and FLAN-T5 has better general in-context learning performance due to massive multitask instruction finetuning. Note that during this process, FLAN-T5 has already been finetuned on all the tasks we used for both training and evaluations, thus all evaluations for FLAN-T5 are in-domain. In addition, the behavior at finetuning is also quite different because the task is already finetuned on the target task, resulting in smaller parameter changes and less forgetting. Despite this, ProMoT still achieves the same performance on RTE as standard finetuning with less forgetting on web questions QA, translation and summarization. PaLM and GPT-3 are PLMs with better in-context learning performances but they are very resource intensive to use and have no open-sourced checkpoints.

From: ICLR.cc/2023/Conference/Paper5395/Authors
title: Response to Reviewer 7Esg (2/3)
comment: > Only two downstream datasets are concerned in this paper --- an NLI dataset and a translation dataset. As described in the paper, the proposed method cannot consistently preserve the in-context learning performance on the all the other datasets considered. Besides the related task, it's hard to understand or explain why the fine-tuned model improves or deteriorates the performance of other tasks. The results seem to be a bit random to me, e.g. why training on RTE improves XSum but not triviaQA. This makes the proposed method unpredictable. Thus, more analysis and experiments are needed to explore the positive transfer and negative interference with this method.

We indeed acknowledge that the relationship between very different tasks are poorly understood by the NLP community. In this work, we observe that ProMoT generalizes over very distinct tasks where traditional finetuning fails. In some cases the relationship between such tasks are obvious: for example translation tasks between different language pairs (Table 3). Some of these generalizations are less obvious, such as the transfer from NLI to summarization (which is a remarkably robust phenomenon that shows up in several different experimental setups on different pretrained models including mT5 and T5). The goal of this work is to understand the cause of forgetting in LLM finetuning and create the ProMoT method that alleviates such forgetting, which is indeed successful in almost all the tasks compared to standard finetuning. In addition, we report the surprising generalization ability of ProMoT across very different tasks, which has not been seen in previous works with a single finetuning set. 
As the reviewer rightly pointed out, such new generalization ability begs an in-depth and systematic study on the semantic relation between very different tasks. However little is known about this direction. [1] has done some analysis on relations between image tasks, while [2] studies the transfer between different styles of QA tasks and [3] studies the transfer between QA, classification and sequence labeling tasks. However, they characterize task relations using the transfer ability using standard finetuning. As demonstrated in this work, this is not the best approach when the tasks have very different format, where standard finetuning does not transfer. While we agree that the relation between very different tasks is an important area of research that is opened up because of ProMoT’s surprising generalization abilities, we believe that it is out of the scope of our current paper. In particular, it is important to report to the ICLR community the cause of forgetting in LLM finetuning, the ProMoT method and its properties in alleviating LLM forgetting and enhancing generalization properties. The detailed and systematic study of semantic and transfer relations between different tasks is definitely worthy of an independent and in depth investigation in its own right. 
Within the scope of this work, we believe that the condition for transfer to happen in standard finetuning is that the tasks share both the format and their semantic content (for example RTE→ CB as in table 2). The condition for transfer to happen in ProMoT is simply their shared semantic content, while the format becomes less important. We observed several robust phenomena that support this intuition. For example there is a robust transfer from RTE to summarization tasks, and from English-French to English-Romania and English-Germany using ProMoT, but not using standard finetuning. While there seems to be some conflict between translation tasks and summarization tasks, which makes sense because summarization involves semantic compression which is not the case for translation. 

[1] https://arxiv.org/pdf/2110.02095.pdf

[2] https://arxiv.org/pdf/2005.00700.pdf

[3]https://aclanthology.org/2020.emnlp-main.635.pdf

From: ICLR.cc/2023/Conference/Paper5395/Authors
title: Response to Reviewer 7Esg (1/3)
comment: We thank the reviewer for their thoughtful comments, suggestions and questions. We added additional experimental results, including results on T5, FLAN-T5 and a more detailed gradient analysis. We also improved our writing and added citations to the paper in the hope that we adequately addressed the reviewer’s concerns. We answer each of their questions in detail as follows:  

> Authors claim that their proposed method (ProMoT) can preserve the in-context learning ability of a pre-trained LM after fine-tuning, however, the experiments conducted in this paper are not strong enough to support this claim.

We do not claim that ProMoT completely keeps all the in-context learning performances of the pretrained model intact. The main result of our work is that we discovered an important cause of forgetting of ICL during standard LLM finetuning, which is format specialization. By absorbing the format learning into tunable prompts, we can finetune the model on the same datasets but the forgetting of ICL on other tasks are greatly alleviate compared to standard finetuning, which is indeed the case in the overwhelming majority of tasks and experimental setups. In addition, we discovered that ProMoT has surprising positive transfer across very different tasks that are not seen in standard finetuning. 

> Authors might want to consider more baseline methods, e.g. fine-tuning with pre-training objectives, stronger parameter-efficient tuning methods (e.g. LoRA (https://arxiv.org/abs/2106.09685) and MAM-Adapter (https://arxiv.org/abs/2110.04366)). There might be other baselines I miss here.

We thank the reviewer for this suggestion. ProMoT is indeed a general framework that is not limited to prompt-tuning. Any parameter efficient methods can be made compatible with ProMoT similar to prompt-tuning, where we off-load the learning of task-specific formats to the parameter efficient part at the first stage of ProMoT, such as tunable prompts or Adapters. However we believe that conceptually it is enough to demonstrate ProMoT on prompt tuning, where the tunable prompts can be very flexibly combined or separated with the model. 
Fine-tuning with pre-training objectives is an interesting alternative. However it increases both the compute cost during finetuning and the complexity of the data preparation pipeline. In addition, it introduces an additional hyper parameter that is the mixture ratio between the finetuning task and the pretraining task. As we demonstrate in this work, a substantial part of forgetting originates from format specialization on the finetuning task. We do not see this method addressing this issue directly thus it could prove to be a very inefficient method to solve the forgetting problem. 
We added citations and discussion of these works in our revised version.

> Authors only considered the gradient alignment of the very first step between two fine-tuned models using different datasets of RTE to make the conclusion that format learning only happens at first, which is not convincing.

We apologize for the confusion. In addition to the gradients, we also studied the model’s outputs themselves during standard finetuning in Figure 2, where we directly observe the phenomenon of format specialization. As for the gradient analysis in Figure 3, we not only considered the gradient alignment of the very first step, but also compared it to the alignment at 400 steps. We show that the gradients are aligned at the first step and then diverge at the 400 step, indicating that format learning happens at the very first stage of finetuning, which makes intuitive sense because the format, which is shared by every data point in the finetuning set, causes a strong gradient signal. In the revision, we also included an additional figure to compare the gradient alignment on step 0, 100, 200, 300 and 400 in Figure 8 in the Appendix. As we can see from the new figure, gradient alignment drops significantly after 300 steps which is matched with Figure 2 where the true and false ratio increases before 300 steps and then remains relatively stable.

From: ICLR.cc/2023/Conference/Paper5395/Authors
title: Response to Reviewer bHim
comment: We thank the reviewer for their thoughtful comments and suggestions. We streamlined the writing and added new experimental results to address the reviewer’s concerns. In the following, we answer the reviewers questions in detail: 

> Some parts are not clearly written. For example, I am confused about ProMoT + 1-shot setting. This paper mentioned that "1-shot sample from training split will be prepended", what is the difference between "ProMoT + 1-shot" on the 1-shot experiment and "ProMoT" on the 1-shot experiment?

We thank the reviewer for pointing out these confusing notations. We are indeed doing very different experiments in these two setups. 
Under the “ProMoT” column, we are applying the ProMoT 2-stage finetuning method without any modifications. After we obtain the trained model checkpoint from ProMoT, we evaluate it on out-of-domain in-context few-shot tasks similar to evaluating a pretrained model. Here we measure both its 1-shot and 4-shot performances. 
Under the “ProMoT+1shot” column, we are exploring combing trainable soft prompts with fixed natural language prompts during training: we prepend a 1-shot natural language example together with the learnable soft prompt during training. After the training is done, when we evaluate the model on out-of-domain tasks, both of these prompts from the training set are removed and the model itself is evaluated with a new 1-shot prompt, now featuring an example from the evaluation set. 
We emphasize that the pretrained, finetuned, ProMoT and 1-shot ProMoT trained models are evaluated with the same prompts to make the performances comparable. 
We updated the paper and added explanations to clarify this part. 

> It is a bit strange to me that most of tasks are English-based tasks, while this paper chose a multilingual model mT5. I would be more willing to see the method applied on English-based pretrained LMs.

We chose mT5 because there are several translation tasks. To address the reviewer’s request on English-based pretrained models, we added additional results using T5 XXL on RTE in Table 9, where we see that ProMoT still significantly outperforms standard fine-tuning without severe forgetting and achieves higher accuracy on RTE compared with prompt-tuning. In addition, finetuning on RTE again leads to improved in-context performances on summarization tasks compared to the pretrained model.  We also list the results here:
|      | pretrained | prompt-tuning on RTE | fine-tuning on RTE | ProMoT on RTE|
| :---        |    :----:   | :----: | :----: | ---:|
| RTE      | 	| 91.7|93.5|93.14|
|CB|55.357|55.357|62.5|73.214|
|WiC|49.843|49.843|50|50.784|
|TriviaQA|34.147|34.147|0.018|33.855|
|Web_questions|16.043|16.043|0|15.945|
|WMT En-De|0.134|0.134|0|0.018|
|WMT En-Ro|0.055|0.055|0|0.008|
|XSum|1.261|1.261|	0|	1.791|
|WikiLingua|1.118	|1.118|	0	|2.251|
In addition, we did additional experiments on FLAN-T5 in Table 8, which is a multi-task fine-tuning version of T5 with improved in-context learning abilities. Note that during the multi-task training process of  FLAN-T5, it has already been finetuned on all the tasks we used for both training and evaluations, thus all the evaluations for FLAN-T5 are in-domain. In addition, the behavior at finetuning is also quite different because the task is already finetuned, with smaller parameter changes and less forgetting. Despite this, ProMoT still achieves the highest performance on RTE with less forgetting on web questions QA, translation and summarization. 

> The comparisons with original pretrained LMs are missing, which makes the whole experimental setting (evaluating the finetuned LM's in-context learning ability on various NLP tasks) looks a bit application-wise useless to me. Because we can directly apply LMs to a new task using in-context learning without concerning about the performance of the finetuned LM on this task.

We apologize for the confusion. We explicitly added a column for the pretrained model’s in-context few-shot performances in our new revision. Previously these results were shown under prompt-tuning, which does not change the parameters of the pretrained model. We hope that the new format makes it easier for readers to compare ProMoT with different baseline methods. Table 2 and 3 are updated and table 6-9 now also features a dedicated column for the pretrained model.

From: ICLR.cc/2023/Conference/Paper5395/Authors
title: Response to Reviewer jtVZ (2/2)
comment: > I’m very supervised that the ProMoT trained on WMT En-Fr could work remarkably well on WMT En-De and En-Ro even without training with a one-shot example (the two columns of ProMoT in Table 3). The output languages (De, Ro, Fr) are quite different. Compared with Prompt-tuning which gets less than 5 BLEU points on En-De or En-Ro translation, why does ProMoT perform significantly better? Given an English sentence, how could the model decide which target language to generate? Purely based on the one-shot example?

We first note that the in-context few-shot performance of Prompt-tuning is the same as the pretrained model, since prompt-tuning does not modify the underlying LLM. We updated our tables to make this point more clear. 
The reviewer is rightly surprised by ProMoT’s generalization across languages on translation tasks, as in standard finetuning this would have been difficult, as we demonstrated in Table 3. With ProMoT, the fact that the output language has to be French is largely absorbed into the soft prompt that was learned in the first stage. Therefore in the second stage the model itself can focus on improving its general translation abilities, thus showing improved results even when the model is tested on other languages. The reviewer is correct that in such evaluations, the model detects the correct target language purely using the 1-shot example that was provided in-context. Generic in-context prowess of LLMs is documented in the literature ([1][2][3][4]). With ProMoT, we take this one step further and enhance e.g. all translation abilities beyond the pretrained model. 

>There is no comparison with a prompt-tuning + 1-shot baseline in Table 2 and 3, as a comparison to ProMoT + 1-shot.

We apologize for the confusion. To test the generalization capabilities of different methods, we evaluate on 8 tasks that are not seen during finetuning/prompt-tuning. The comparison that the reviewer requested is indeed present in Table 2 and 3. Prompt-tuning + 1-shot is the same as prompt-tuning in Table 2 and Table 3 since prompt-tuning doesn’t modify pretrained model weights. We rewrote the table to make this more clear. 
On the in-domain evaluation of the finetuned task, we list the performance as follows:


||RTE|En-Fr|
|:---|:----:|---:|
|Prompt-tuning + 1shot|92.78|39.41|
|ProMoT + 1shot|93.86|41.19|


ProMoT indeed outperforms Prompt-tuning + 1-shot on these in-domain evaluations.

[1] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J Liu, et al. Exploring the limits of transfer learning with a unified text-to-text transformer.

[2] Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. mt5: A massively multilingual pre-trained text-to-text transformer.

[3] Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. Improving language understanding by generative pre-training. 2018

[4] Aakanksha  Chowdhery,  et al. Palm: Scaling language modeling with pathways, 2022

From: ICLR.cc/2023/Conference/Paper5395/Authors
title: Response to Reviewer jtVZ (1/2)
comment: We thank the reviewer for their thoughtful comments and suggestions. At a high level, the goal of ProMoT is to build a model that is superior, in a broad class of in-context tasks, to both the pretrained model and the standard finetuned model. For example (as the reviewer rightfully pointed out), with one single supervised dataset on English to French translation, ProMoT vastly outperforms both the pretrained and the standard finetuned models on unseen translation tasks (English-Germany and English-Romania). Because of this superior generalization capability we believe ProMoT is a promising method that can enhance a general ability such as translating any language pairs, rather than a specific ability such as translating English to French, even when we only have a single English-French training set. ProMoT also exhibits this type of generalization when finetuned on RTE, where the resulting model has enhanced grounding capabilities that is demonstrated as an improvement on in-context summarization tasks (Table 1,2). Such long range generalization has not been documented before, especially when the model is finetuned on a single task. We believe this is a phenomenon that is worth reporting to the ICLR community. We updated the paper with additional experimental results and improved presentation to address the questions raised by the reviewer. 

In the following, we address each of the reviewer’s questions in detail:
> Evaluating prompt-tuning based or standard fine-tuning methods under the cross-task prediction setting is questionable. These methods are not mainly designed and used in this cross-task setting, making the comparison less meaningful.

We agree with the reviewer that prompt-tuning and standard fine-tuning are not ideal for cross-task settings. This is exactly the limitation that we are aiming to transcend in this work. Our new finetuning method, ProMoT, generalizes much better than traditional prompt-tuning or finetuning across task boundaries, as demonstrated in table 2 and 3. 
In addition, we added new results on multitask finetuning (Table 7 of our new version), which is the standard approach to improve LLM’s in-context abilities. We show that multitask ProMoT generalizes better than standard multitask finetuning on most unseen tasks. 

> Even though the proposed method slightly improves the prompt-tuning baseline on the target tasks, most of the cross-task performance is really low (e.g., less than 5 BLEU in RTE->WMT), which means we cannot use the tuned model on any of these downstream tasks.

When the evaluation task (e.g. WMT) is semantically unrelated to the finetuning task (e.g. RTE), our goal is to preserve the in-context performance of the pretrained model (mT5) which depends on the underlying pretrained model. Therefore the performance of ProMoT in this case should be compared to the pretrained model (which is low) and standard finetuning (which is close to zero). We show that ProMoT preserves a significant portion of the pretrained performance in these cases, while standard finetuning almost completely destroys in-context abilities. This alleviated forgetting is the reason that ProMoT is better than standard finetuning as a method that can improve pretrained models while keeping their status as general purpose models. 

To get better performances for a class of downstream tasks, we show in Figure 5 and Table 7 (new result) that with multi-task or sequential ProMoT training on multiple tasks, we can obtain a model checkpoint with generally much better few-shot performance on unseen tasks (e.g. 18+ BLEU on WMT).

>The proposed method still requires updating the whole model parameters in stage 2, which is computationally heavy for large models.

First we want to clarify that although prompt-tuning is parameter-efficient, it is not significantly computationally efficient than full fine-tuning and ProMoT and still requires a full back propagation through the model during training. Second, our goal in this work is not to achieve better parameter or compute efficiency compared to standard finetuning, but to obtain an improved model that is superior to both the pretrained model and a standard finetuned model in a broad class of in-context tasks. This goal precludes using prompt tuning only, which does not change the pretrained model at all. The reason we employed prompt tuning is not because it is parameter efficient, but because the soft prompt can absorb format learning from a specific finetuning task that would otherwise cause severe forgetting of in-context abilities. We demonstrate that when the model itself is not responsible for learning the format, the semantic skill that it has gained on a single finetuning dataset can generalize to very different tasks.

From: ICLR.cc/2023/Conference/Paper5395/Reviewer_jtVZ
confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
summary_of_the_paper: This paper shows that LLMs suffer from the loss of in-context prediction due to the overfitting to the output format of the fine-tuned task, and propose a 2-stage tuning strategy to mitigate the loss. Fine-tuning experiments on two target tasks (e.g., binary text classification, translation) show the better in-context performance of their proposed method over the standard model fine-tuning and prompt tuning baselines. 
strength_and_weaknesses: Strength:
- The examination of format overfitting issue is relatively new but also expected. 

Weaknesses:
- Evaluating prompt-tuning based or standard fine-tuning methods under the cross-task prediction setting is questionable. These methods are not mainly designed and used in this cross-task setting, making the comparison less meaningful.
- Even though the proposed method slightly improves the prompt-tuning baseline on the target tasks, most of the cross-task performance is really low (e.g., less than 5 BLEU in RTE->WMT), which means we cannot use the tuned model on any of these downstream tasks.
- The proposed method still requires updating the whole model parameters in stage 2, which is computationally heavy for large models. 

clarity,_quality,_novelty_and_reproducibility: - The standard fine-tuning method obviously would not work well in the cross-task prediction setting, because it is often used when we have labeled data in the target task. Besides, one benefit of prompt-tuning is that we could share the whole LM, and use different trainable prompts for different tasks. However, this paper evaluates the prompt-tuning in the cross-task setting (i.e., train on the source task, evaluate on the target task without target-task prompts), which is not what prompt-tuning is normally used – Essentially it’s evaluating the pre-trained model’s in-context capability on target tasks.  
- On the other hand, the motivation of parameter-efficient prompt-tuning is to avoid training the whole LM but obtain comparable performance. In-context capability usually occurs in really large LMs, which are computationally expensive to train. 
- I’m very supervised that the ProMoT trained on WMT En-Fr could work remarkably well on WMT En-De and En-Ro even without training with a one-shot example (the two columns of ProMoT in Table 3). The output languages (De, Ro, Fr) are quite different. Compared with Prompt-tuning which gets less than 5 BLEU points on En-De or En-Ro translation, why does ProMoT perform significantly better? Given an English sentence, how could the model decide which target language to generate? Purely based on the one-shot example?
- There is no comparison with a prompt-tuning + 1-shot baseline in Table 2 and 3, as a comparison to ProMoT + 1-shot.   

summary_of_the_review: The experimental setting, baselines in comparison, and some results need more justification. 
correctness: 2: Several of the paper’s claims are incorrect or not well-supported.
technical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
empirical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
flag_for_ethics_review: NO.
details_of_ethics_concerns: NA
recommendation: 3: reject, not good enough

From: ICLR.cc/2023/Conference/Paper5395/Reviewer_bHim
confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
summary_of_the_paper: This paper first investigated how finetuning pre-trained language models affects the in-context prompt learning. Then it proposed the Prompt Tuning with Model Tuning (ProMoT) method which aims to preserve the in-context learning abilities of the pretrained model during finetuning. Experiments are conducted on a diverse set of NLP tasks. Results illustrate the ProMoT's remarkable generalization ability on tasks that have different formats. 
strength_and_weaknesses: Strengths
1. This paper tackles an interesting research problem and the results are promising
2. Experiments are conducted on a diverse set of NLP tasks.

Weaknesses
1. Some parts are not clearly written. For example, I am confused about ProMoT + 1-shot setting. This paper mentioned that "1-shot sample from training split will be prepended", what is the difference between "ProMoT + 1-shot" on the 1-shot experiment and "ProMoT" on the 1-shot experiment?
2. It is a bit strange to me that most of tasks are English-based tasks, while this paper chose a multilingual model mT5. I would be more willing to see the method applied on English-based pretrained LMs.
3. The comparisons with original pretrained LMs are missing, which makes the whole experimental setting (evaluating the finetuned LM's in-context learning ability on various NLP tasks) looks a bit application-wise useless to me. Because we can directly apply LMs to a new task using in-context learning without concerning about the performance of the finetuned LM on this task.
clarity,_quality,_novelty_and_reproducibility: This paper does not look very clear to me in various parts. The proposed method is original.
summary_of_the_review: This paper proposed the Prompt Tuning with Model Tuning (ProMoT) method which aims to preserve the in-context learning abilities of the pretrained model during finetuning. Experiments are conducted on a diverse set of NLP tasks. Results illustrate the ProMoT's remarkable generalization ability on tasks that have different formats. However, some important baselines are missing and more experiments on different LMs are needed. 
correctness: 3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.
technical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
empirical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
flag_for_ethics_review: NO.
recommendation: 5: marginally below the acceptance threshold

From: ICLR.cc/2023/Conference/Paper5395/Reviewer_7Esg
confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
summary_of_the_paper: This paper proposes a fine-tuning method that tries to preserve the in-context learning ability of a pre-trained language model (PLM). Specifically, it's a two-stage fine-tuning method that first freezes the PLM and only tunes the added soft prompt vectors, then it freezes the soft prompts and fine-tunes the PLM. It picks two downstream tasks (NLI and translation) and apply the proposed fine-tuning methods and evaluate the in-context learning (few-shot) performance on a range of other tasks.
strength_and_weaknesses: **Strengths:**
1. The paper is clearly written and easy to understand.
2. The proposed method improves over full fine-tuning in terms of preserving the in-context learning ability.

**Weaknesses:**
Authors claim that their proposed method (ProMoT) can preserve the in-context learning ability of a pre-trained LM after fine-tuning, however, the experiments conducted in this paper are not strong enough to support this claim.
1. Only two downstream datasets are concerned in this paper --- an NLI dataset and a translation dataset. As described in the paper, the proposed method cannot consistently preserve the in-context learning performance on the all the other datasets considered. Besides the related task, it's hard to understand or explain why the fine-tuned model improves or deteriorates the performance of other tasks. The results seem to be a bit random to me, e.g. why training on RTE improves XSum but not triviaQA. This makes the proposed method unpredictable. Thus, more analysis and experiments are needed to explore the positive transfer and negative interference with this method.
2. Given ProMoT is proposed to preserve the ICL ability of a PLM, a PLM with strong in-context learning performance is desired to be used for experiments. However, authors choose to use mT5, which has poor or even chance in-context learning performance.
3. Authors might want to consider more baseline methods, e.g. fine-tuning with pre-training objectives, stronger parameter-efficient tuning methods (e.g. LoRA (https://arxiv.org/abs/2106.09685) and MAM-Adapter (https://arxiv.org/abs/2110.04366)). There might be other baselines I miss here.
4. Authors only considered the gradient alignment of the very first step between two fine-tuned models using different datasets of RTE to make the conclusion that format learning only happens at first, which is not convincing. 
clarity,_quality,_novelty_and_reproducibility: Paper is clearly written. The methods and baselines are reproducible.
However, experiments are not well executed to demonstrate that ProMoT can preserve the ICL ability of the pre-trained model.

summary_of_the_review: Authors proposed an interesting method and the motivation is good, however, there are not sufficient experiments to support the claims in the paper. Thus I recommend a rejection.
correctness: 2: Several of the paper’s claims are incorrect or not well-supported.
technical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
empirical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
flag_for_ethics_review: NO.
recommendation: 3: reject, not good enough

From: ICLR.cc/2023/Conference/Paper5395/Reviewer_7DnR
confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
summary_of_the_paper: The paper attempts to explain why the in-context ability is lost in fine-tuning, and designs a method to keep the ability. Experimental results on a bunch of benchmarks demonstrate the effectiveness of the proposed method.



strength_and_weaknesses: Strength:
a unique perspective to PLMs
an interesting method
abundant empirical studies

Weakness:
some results are inconsistent without explanations. 
clarity,_quality,_novelty_and_reproducibility: A well-written paper with interesting ideas. The method is simple and thus others can easily reproduce the results. 
summary_of_the_review: First of all, I am interested in the paper, as it demonstrates what happens in fine-tuning LMs (though only a small step). The designed method is neat and supported by the empirical studies. 

My concerns lie in the experimental results. I am almost convinced by the results that the proposed method can isolate format and semantics in tasks, but the experimental results also confused me sometimes, because it seems that the performance of the models is a little bit random on the datasets. For example, in Table 3, why the Standard Fine-tuning outperforms ProMoT and Prompt-tuning on WiC? On the similar classification task CB, it is the worst one. Similarly, in Table 2, why Prompt-tuning is significantly better than ProMoT on MT tasks? I guess when comparing Prompt-tuning and ProMoT on MT, they are almost equivalent, right? 

correctness: 3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.
technical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
empirical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
flag_for_ethics_review: NO.
recommendation: 6: marginally above the acceptance threshold