Paper: Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs (ICLR-2024)

Reviewer comment (Evaluation):
The evaluation for the JSON format task's "Format accuracy" measures whether there is any subsequence of the generated text that is valid JSON, rather than assessing whether the whole output is a valid JSON. This allows cases where natural text is intermingled with valid JSON to be scored as accurate, which is highly debatable.

Author response:
**Q1: The evaluation measures whether there is any subsequence of the generated text that is valid json. It is highly debatable whether a model that would generate natural text intermingled with valid json is actually accurate at generating json.**  

Thanks for your feedback regarding our evaluation. To clarify, our decision to evaluate subsequences as valid JSON strings stems from the model's behavior during generation. We observed that when the model successfully follows the instruction to generate a JSON string, it tends to repeat the correct JSON strings until reaching the maximum token limit. For instance, an output might look like ‘{"name":"Mary", "occupation":"doctor"} {"name":"Mary", ...’. Moreover, models occasionally preface JSON strings with natural language text, such as ‘The answer is {“name”, “Mary”, …’. To accommodate these variations and fairly assess the model's capability, we chose to evaluate whether any subsequence, starting from '{' and ending with '}', forms a valid JSON string. 

To provide a comprehensive view of the model's performance, we have included all generation results by LLAMA-7B on the JSON Formatting task in the link of https://anonymous.4open.science/r/PASTA-10E9/model_generations/json_formatting/llama7b_pasta_generation.json (the table blew shows a few examples). We invite the reviewer to examine these results in detail. These generation results showcase the practical effectiveness of the PASTA approach in guiding the model to generate valid JSON structures. 

| Prompt | PASTA Generation | 
| :-----: | :-----: | 
| David is an attorney in San Antonio. He doesn’t dress up for work and he doesn’t shred his client files. He was caught throwing personal information…{instruction} | {"name": "David", "occupation": "attorney"}\n{"name": "David", "occupation": "attorney"}... |
 | Juris is an associate professor at the Faculty of Information, University of Toronto. He is a co-author of Readers' Advisory Service in North American…{instruction} | {"name": "Juris", "occupation": "Associate Professor"}\n{"name": "Juris", "occupation": "Associate Professor"}... | 
| Gregory is an educational psychologist whose major concern is with how people learn, ie skills and knowledge development especially in…{instruction} | {"name": "Gregory", "occupation": "Educational Psychologist"}\n{"name": "Gregory", "occupation": "Educational Psychologist"}...|

Reviewer comment (Evaluation):
The evaluation for the JSON format task's "Prediction accuracy" checks only whether the desired string is present anywhere in the JSON values. This means that an output containing, for example, all of the words in the context within one large JSON would score well, even if it does not match the intended target precisely.

Author response:
**Q2: The prediction accuracy checks if the desired string is present anywhere in the json values. As such, an answer that would print out e.g. all of the words in the context into one large json would score well.**  

In the biographical data, there are 28 target labels, each of which is a short string, e.g. professor, teacher, nurse. In practical generations, models tend to generate JSON values that contain more detailed information derived from the context, such as “assistant professor of computer science”, “English teacher”, “retired nurse”, etc (see examples in the table of Q1). To accommodate these variations given different contexts, we evaluate the prediction accuracy by checking if the short string of labels is present in the JSON values.

To further ensure a rigorous evaluation, we reevaluate the results with an additional criterion: the JSON values must be concise, with a word limit (e.g., 10 words for ‘professor’ class and 3 words for the others). A re-evaluation of the prediction accuracy with the word limit criterion yielded an accuracy of 84.42% for LLAMA-7B using PASTA, only marginally lower than the originally reported 85.09% in Table 1. This minimal discrepancy can support our claim that PASTA effectively guides the model to generate relevant and concise JSON values. The detailed generation results, available at https://anonymous.4open.science/r/PASTA-10E9/model_generations/json_formatting/llama7b_pasta_generation.json , demonstrate that the occurrence of the model outputting entire contexts as JSON values is much infrequent. 

We believe these measures robustly evaluate the model's prediction accuracy and effectively mitigate the concern of scoring well through irrelevant verbosity. We invite the reviewer to review the provided results to further assess the effectiveness of our approach.

Reviewer comment (Evaluation):
These flaws in evaluation are troubling because a high score on these metrics may be accompanied by a substantial decrease in overall fluency and perceived quality of the model, which is misaligned with the paper's stated contribution of enhancing an LLM’s ability to follow user instructions.

Author response:
**Q4: Clarification of the results in Figure 2b** 

Figure 2b indicates the drawbacks of overemphasizing. When users overemphasize specific parts by steering too many heads (e.g., 294), it leads the model to only focus on the specific information while ignoring other parts. This imbalance results in the deterioration of overall generation quality. In the example of Pronouns changing task, when 294 heads are steered, the model becomes overly fixated on emphasized parts, leading to repeated generations, such as “Lindsey is a textile worker. They are a textile worker. They are …”, This deterioration reflected in the decreased consistency score of 7.09 and fluency score of 3.16. Therefore, Figure 2b suggests applying PASTA to a moderate number of heads (typically 30 to 150) is ideal, which strikes a balance between effective emphasis and generation quality.

Reviewer comment (Novelty):
The paper’s main focus on generating text that follows specific instructions overlaps with existing instruction-tuning research. While the authors claim that "PASTA can be used in addition to these approaches to improve some aspects of model steerability," this requires more substantiation. It is plausible that instruction fine-tuning already modifies attention weights to attend closely to the operator's instructions, and it is unclear whether PASTA’s improvements would transfer to such models.

Author response:
**Q5: The notion that PASTA improvements would transfer to such models would thus need to be tested.**
 
We test PASTA’s applicability to Vicuna-7B-v1.3, an instruction-tuned model from LLAMA-7B.  We apply PASTA using attention heads selected from LLAMA-7B profiling, including intersection and task-specific heads. Then we evaluate the performance of Vicuna across four tasks. The table below presents the results. 

| Models | JSON. F. Acc | JSON Pred. Acc | Prons. Acc. | Prons. Consistency | BiasBios Acc | CounterFact ES | CounterFact PS | 
| :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: |  
| LLAMA Zero-shot | 60.00 | 54.94 | 71.84 | 22.29 | 87.36 | 58.50 | 52.03 | 
| LLAMA PASTA(intersection) | 96.64 | 85.09 | 96.42 | 21.91 | 95.28 | 99.60 | 99.57 | 
| Vicuna Zero-shot | 65.41 | 61.78 | 95.74 | 21.83 | 90.74 | 61.10 | 52.46 | 
| Vicuna PASTA(intersection) | 66.09 | 56.00 | 98.82 | 22.59 | 96.44 | 99.80 | 99.80 | 
| Vicuna PASTA(task-specific) | 90.54 | 86.53 | 98.62 | 22.48 | 97.42 | 99.82 | 99.74 | 

The results demonstrate that the attention heads selected for LLAMA-7B effectively steer Vicuna-7B, indicating that re-profiling is not necessary for instruction-tuned models. Notably, when steering task-specific heads selected from LLAMA profiling, PASTA significantly enhances Vicuna's performance across all tasks. This evidence shows that PASTA can complement instruction tuning to further improve the model ability of instruction following.

Reviewer comment (Experiments):
The claim about PASTA’s utility alongside instruction-tuning needs to be tested empirically. Additionally, the cost of the model profiling phase should be compared to the cost of a lightweight instruction-tuning setup.

Author response:
**Q6: Furthermore, the cost of the model profiling phase should be compared to the cost of a lightweight instruction-tuning setup.**

Profiling for PASTA is an inference-only process, conducted on a small subset of the training set. For instance, profiling all heads of LLAMA-7B with 1000 samples requires 1000 * 32 * 32 inferences. In contrast, fine-tuning a model like Vicuna-7B on a dataset such as 128k ShareGPT data for 8 epochs demands an equal number of forward passes, along with additional backward passes that incur much higher memory footprints. Fine-tuning also involves multiple trials of hyperparameter tuning, whereas profiling is performed only once for each model. Moreover, fine-tuning requires maintaining separate copies of fine-tuned model weights while PASTA does not incur any additional model checkpoints and can be applied plug-and-play. 

Meanwhile, the robustness of head performance ranking to sample variance allows us to further reduce the sample size for profiling (e.g., $|\mathcal{D}|=200$). The new table below presents the PASTA performance on the JSON Formatting task when re-profiling with $|\mathcal{D}|=200$ samples. We can see PASTA still achieves superior performance when profiling with much fewer examples. 

| Model | Profiling Sample size | JSON F. Acc | JSON Pred. Acc |
| :-----: | :-----: | :-----: | :-----: | 
| LLAMA Zero-shot | N/A | 60.00 | 54.94 | 
| LLAMA w. PASTA | 1000 | 95.56 | 86.83 | 
| LLAMA w. PASTA | 200 | 95.04 | 84.17 | 
| Vicuna Zero-shot | N/A | 65.41 | 61.78 |
| Vicuna w. PASTA | 200 | 98.56 | 94.56 |

Reviewer comment (Evaluation):
The PASTA method does not necessitate alterations in model parameters, resulting in enhanced performance efficiency; however, a pertinent issue arises concerning the potential disruption of the established generative law from language modeling via manual attention steering. This makes a conjecture that such intervention could inadvertently impair other abilities of LLMs, such as generative fluency and reasoning.

Author response:
**Q2: This makes a conjecture that such intervention could inadvertently impair other abilities of LLMs, such as generative fluency and reasoning.**
 
Thanks for your comment. We fully understand the importance of preserving generative fluency and quality while enhancing task-specific performance with PASTA. To ensure this, we employ two metrics to evaluate the quality of PASTA generations across three natural language generation tasks (Prons. Changing, BiasBios, and CounterFact). 

1. Fluency Evaluation (Meng et al., 2022): As mentioned in Section 4, we assess the fluency of all generations (the average bigram and trigram entropy of generations), and exclude results with a fluency score below 3.0. This step effectively eliminates degenerated or repetitive generations from consideration. 

2. Consistency Metric: We employ an additional consistency metric (introduced by Hernandez et al., (2023)), which measures the average tf-idf similarity between the generated text and reference texts of full dataset. This metric helps us measure how well the generated text aligns with overall contextual inputs in terms of content and style (higher is better). 

The table below presents a few examples of LLAMA-7B generation with PASTA and their fluency and consistency scores on the Pronouns changing task (please see Appendix D for more examples).  

| Prompt | PASTA generation | Flue. / Cons. | 
| :-----: | :-----: | :-----: | 
| Sawako is a teacher at Sakura High. She is originally the adviser of the wind instrument club but then she was forced to become the adviser of the light music club…{instruction} | Sawako is a teacher at Sakura High. They are originally the adviser of the wind instrument club but then they were forced to become the adviser of the light music club… | 5.21 / 17.53 | 
| Catherine is a Sydney-based journalist. She has written for both Fairfax and News Ltd. She has never listened to Alan Jones' radio show…{instruction} | Catherine is a Sydney-based journalist. They have written for both Fairfax and News Ltd. They have never listened to Alan Jones' radio show… | 4.91 / 15.30 | 
| Cynthia is an assistant professor of History at UND. Her book, Gender and Generation on the Far Western Frontier, traces changes in gender roles…{instruction} | Cynthia is a historian who studies the history of the American West. They are currently working on a book about the history of the American West… | 4.51 / 23.24 |
| Shahid is an immigration attorney in Helena, Montana and represents…{instruction} | Shahid is a they who is a they who is a they who is a they… | 2.05 / 5.56 | 

From the examples, we can see that repetitive or meaningless generations receive low fluency (below 3.0) and consistency (below 8.0). The generations with high fluency (around 4.5) and consistency (above 13) are meaningful and readable. The following table presents the average fluency and consistency evaluation across the mentioned tasks:

| Task | Metric | Zero-shot| PASTA | 
| :-----: | :-----: | :-----: | :-----: | 
| Prons. Changing | Acc | 71.84 | 92.30 | 
| Prons. Changing | Consistency | 22.29 | 22.37 | 
| Prons. Changing | Fluency | 6.10 | 6.07 | 
| BiasBios | Acc | 87.36 | 95.28 |
| BiasBios | Consistency | 13.02 | 14.25 |
| BiasBios | Fluency | 3.98 | 4.05 | 
| CounterFact | Efficacy score | 58.50 | 99.60 |
| CounterFact | Paraphrase score | 52.03 | 99.57 |
| CounterFact | Consistency | 11.64 | 19.29 |
| CounterFact | Fluency | 4.96 | 4.89 |

The results show that PASTA achieves comparable consistency and fluency scores to zero-shot prompting. This indicates that PASTA effectively maintains the generative quality and fluency while significantly improving the task efficacy. 

Meanwhile, we would like to kindly remind the reviewer that Section 5.3 (Figures 3a and 3b) has discussed the reviewer’s conjecture. The generative fluency can be impacted only when users overemphasize highlighted input spans by steering too many heads (e.g., 294). Such excessive emphasis can cause the model to focus narrowly on specific information while ignoring other parts of inputs, decreasing consistency and fluency. Therefore, we recommend applying PASTA to a moderate number of heads: typically between 30 to 150. Steering this range of heads already yields significant improvement in task performance while maintaining high generative quality, striking a good balance between emphasis efficacy and generative fluency.

Reviewer comment (Experiments):
The current experimental design appears to be limited in scope. For a more comprehensive evaluation, it would be beneficial to test the effectiveness of the proposed method on diverse large language models, including but not limited to, 7B-LoRA, 13B-LoRA, and 13B, across specific tasks.

Author response:
**Q3: it would be beneficial to test the effectiveness of the proposed method on diverse large language models, including but not limited to, 7B-LoRA, 13B-LoRA, and 13B, across specific tasks.**
 
We conduct experiments with LLAMA-13B and Vicuna-7B to further evaluate the effectiveness of PASTA across all tasks. The following table presents the performance comparison for LLAMA-13B (Flues./Cons. means fluency/consistency scores). 

| Method | JSON Formatting | Prons. Changing | Prons. Changing | BiasBios | BiasBios | CounterFact | CounterFact | 
| :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | 
|  | F. / P. Acc | Acc / A. Acc | Flue./Cons. | Acc | Flue./Cons. | ES / PS | Flue./Cons. | 
| Zero-shot | 45.48/43.16 | 65.03/60.90 | 6.11/22.50 | 85.80 | 4.52/13.89 | 47.86/44.14 | 5.06/11.10 |
| Few-shot | 39.80/3.56 | 82.33/80.71 | 5.19/22.44 | 88.38 | 4.91/18.04 | 90.63/65.49 | 5.93/11.13 | 
| PASTA (multi-task) | 98.74/89.88 | 97.56/96.78 | 6.07/22.95 | 95.34 | 4.61/15.72 | 99.38/99.30 | 5.07/17.04 | 

The results demonstrate that PASTA significantly improves the performance of LLAMA-13B across all tasks, indicating its generability to larger model sizes. Meanwhile, PASTA maintains or improves fluency and consistency scores compared to zero-shot prompting on three of the tasks, suggesting that PASTA does not impair model generative ability and fluency. 

We further test PASTA’s applicability to Vicuna-7B-v1.3, which is instruction-tuned from LLAMA-7B. We apply PASTA using attention heads selected from LLAMA-7B profiling (including multi-task and task-specific heads). In this way, we evaluate if the heads selected from the base models are transferable to an instruction-tuned model, thereby avoiding the re-profiling. The table below presents the performance of Vicuna across all tasks.  

| Method | JSON Formatting | Prons. Changing | Prons. Changing | BiasBios | BiasBios | CounterFact | CounterFact | 
| :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | 
|  | F. / P. Acc | Acc / A. Acc | Flue./Cons. | Acc | Flue./Cons. | ES / PS | Flue./Cons. | 
| LLAMA Zero-shot | 60.00/54.94 | 71.84/66.28 | 6.10/22.29 | 87.36 | 3.98/13.02 | 58.50/52.03 | 4.96/11.64 | 
| LLAMA PASTA(multi-task) | 96.64/85.09 | 96.42/95.84 | 5.43/21.91 | 95.28 | 4.05/14.25 | 99.60/99.57 | 4.89/19.29 |
| Vicuna Zero-shot | 65.41/61.78 | 95.74/94.74 | 5.75/21.83 | 90.74 | 4.20/16.00 | 61.10/52.46 | 5.07/12.76 | 
| Vicuna PASTA(multi-task) | 66.09/56.00 | 98.82/98.08 | 5.87/22.59 | 96.44 | 4.60/16.79 | 99.80/99.80 | 5.11/21.26 | 
| Vicuna PASTA(task-specific) | 90.54/86.53 | 98.62/98.04 | 5.81/22.48 | 97.42 | 4.56/16.24 | 99.82/99.74 | 5.03/20.28 | 

The results demonstrate that the attention heads selected for LLAMA-7B effectively steer Vicuna-7B, indicating that re-profiling is not necessary for instruction-tuned models. Notably, when steering task-specific heads selected from LLAMA profiling, PASTA significantly enhances Vicuna's performance across all tasks. This evidence shows that PASTA can complement instruction tuning without necessitating re-profiling.

Reviewer comment (Experiments):
There have been previous studies on improving the standard attention with the prior knowledge, such as syntax-based attention and multiscale attention. These should be considered baselines for comparison, either in related work or experiments. Additionally, although the four tasks previously considered do not adequately represent the breadth of generative tasks commonly utilized as benchmarks, they do not represent the prevailing benchmarks within generative tasks. It is crucial to evaluate the proposed methodology's efficacy by applying it to some commonly used generative tasks, including but not limited to, machine translation and question answering tasks.

Author response:
**Q4: The previous studies on syntax-based attention and multiscale attention should be considered baselines for comparison, either in related work or experiments.**
 
Thanks for your feedback regarding the relevance of prior studies on syntax-based and multiscale attention. Multi-scale attention [3] introduces varied attention scales across different heads, enabling diverse functionalities. Syntax-based attention [4,5], leverages the syntactic structures of inputs to modify attention patterns. However, these attention mechanisms require additional training to adapt to existing LLMs like LLAMA. By contrast, PASTA does not require additional training or alternations in model parameters, which allows PASTA to be seamlessly integrated with existing LLMs. We appreciate your comments on these insightful prior works and will discuss them in the related work of our next version. 

**Q5: It is crucial to evaluate the proposed methodology's efficacy by applying it to some commonly used generative tasks, such as machine translation and question-answering tasks.**
 
Thanks for your valuable feedback regarding more experimental comparisons on commonly used tasks, such as MT and QA. We fully recognize the importance of evaluating our methodology across these standard benchmarks and make every effort to conduct these additional comparisons during the rebuttal phase. However, we cannot access the same computational resources as before. Due to the limited computational capacity and time, we may not be able to finish all these additional experiments on time. Please be assured that we are committed to expanding the scope of our research. We will prioritize completing these experiments and will share the new results as soon as they are available.

Reviewer comment (Experiments):
Did you perform the SFT produce on the models in the experiment?

Author response:
**Q6: Response to other questions.**
 
Regarding the question of supervised fine-tuning (SFT), PASTA is an inference-only method and does require any fine-tuning or re-training. Therefore, we did not perform any fine-tuning for the models. All evaluations are performed on the pre-trained models. 
We will include detailed implementations of accuracy evaluation in the appendix of our next version.

Reviewer comment (Evaluation):
You should present more details in computing accuracy on these used datasets.

Author response:
**Q6: Response to other questions.**
 
Regarding the question of supervised fine-tuning (SFT), PASTA is an inference-only method and does require any fine-tuning or re-training. Therefore, we did not perform any fine-tuning for the models. All evaluations are performed on the pre-trained models. 
We will include detailed implementations of accuracy evaluation in the appendix of our next version.

Reviewer comment (Evaluation):
One thing I am really curious about is whether we have any preferences layer-wise. Since different layers are known to possess different functions. Which layer is more important for attention steering? By Figure 2 it seems that it’s approximately random, is there any explanation for this?

Author response:
Thanks for your great comment. According to our observation (please see Figures 2,4,5,6), there are some layers like layer 19 in LLAMA-7B that can be generally steered for all of the tasks to improve the performance. These layers can be applied to attention steering in task-agnostic manners. In most cases, each task prefers different layers for attention steering, which should be used task-specifically. More fine-grainedly, there is a similar observation at the level of attention heads. Therefore, we propose multi-task model profiling to identify these task-specific/task-agnostic layers and heads. As an initial work, we believe that PASTA can motivate future work on attention steering.

Reviewer comment (Experiments):
The results are on those tasks only where the highlighted instruction is at the end of the prompt (except for the rephrased JSON Formatting task). What is the general effect of having highlighted instruction at the start or some other place in the prompt? This would give an idea if the proposed approach is general enough.

Author response:
**Q2: What is the general effect of having highlighted instruction at the start or some other place in the prompt?**
 
We would like to remind the reviewer that both highlighted input spans in BiasBios and CounterFact tasks are not at the end of the prompts. As mentioned in Section 4, we highlight the first sentence for the BiasBios task, which carries the most information for prediction. In the CounterFact task, we highlight new facts, which are in the middle of prompts. Meanwhile, Table 4 already shows the performance of PASTA on the JSON Formatting task when the highlighted instruction is at the beginning of prompts. These results demonstrate that PASTA consistently improves the performance no matter where highlighted input spans are. 

In the following table, we further present the PASTA performance for LLAMA-7B when the highlighted instruction is at the start of the Pronouns changing prompts. 

| Instruction Position | Method | Acc | All-changed Acc | 
| :-----: | :-----: | :-----: | :-----: |
| Start of prompts  | Zero-shot | 19.63 | 9.82 | 
| Start of prompts | PASTA | 50.96 | 46.06 | 
| End of prompts | Zero-shot | 71.84 | 66.28 | 
| End of prompts | PASTA | 96.42 | 95.84 | 

The results illustrate the sensitivity of model performance to prompt rephrasing – having the instruction at the start for Pronouns changing task degenerates the accuracy of zero-shot prompting to 19.63%. In contrast, PASTA can improve its performance by 31.3%, indicating the effectiveness of PASTA for highlighting instructions at different positions.

Reviewer comment (Writing):
Not a weakness, but there's a typo in footnote 3: access $\textbf{to}$ model weights ...

Author response:
**Q5: Response to other questions.**
 
We appreciate the reviewer for pointing out typos in our paper and other suggestions for the presentation. We will follow these suggestions and make revisions in the next version.

Reviewer comment (Experiments):
Beam search sampling and top-k sampling are much better compared to greedy. The baseline performance would definitely improve using these generation methods. How does PASTA compare to the baselines using these sampling approaches?

Author response:
**Q3: The baseline performance would definitely improve using these generation methods. How does PASTA compare to the baselines using these sampling approaches?**
 
To clarify the reviewer’s question, we apply the greedy search for all of the methods including prompting strategies and PASTA to have a fair comparison. Moreover, in the BiasBios and CounterFact tasks, models mainly use the logit of the first generated tokens to make predictions, and hence greedy search should be used. The following table presents the zero-shot performance of LLAMA-7B on the JSON Formatting and Prons. Changing tasks when applying beam search and topk sampling. 

| Method | Sampling strategy | JSON F./P. Acc | Prons. Acc/A. Acc | 
| :-----: | :-----: | :-----: | :-----: |
| Zero-shot | Topk (k=5) | 35.93 / 26.67 | 59.90 / 54.26 | 
| Zero-shot | Topk (k=50) | 19.73 / 10.42 | 53.80 / 46.58 | 
| Zero-shot | Beam search (# beams=4) | 15.81 / 14.95 | 86.95 / 83.73 |  
| PASTA(multi-task) | Beam search (# beams=4) | 83.97 / 71.11 | 98.06 / 97.36 | 
| PASTA(multi-task) | Gready search | 96.64 / 85.09 | 96.42 / 95.48 | 

From the results, we can see that PASTA still outperforms baselines when using the beam search or topk sampling strategies.

Reviewer comment (Experiments):
*-marked and "" - marked have very low performance. Have the authors tried better markers like <mark> </mark> or <emphasize> </emphasize>. I believe these markers are much more frequent in the training set, and might improve performance.

Author response:
**Q4: \*-marked and "" - marked have very low performance. Have the authors tried better markers like <mark> </mark> or <emphasize> </emphasize>.**
 
In the following table, we present the performance of these marked prompting baselines on LLAMA-7B across four tasks. 

| Method | JSON F./P. Acc | Prons. Acc/A.Acc | BiasBios Acc | CounterFact ES/PS | 
| :-----: | :-----: | :-----: | :-----: | :-----: |
| <mark></mark>-marked | 9.90/9.32 | 28.99/25.25 | 90.22 | 58.08/49.33 | 
| <emphasize></emphasize>-marked | 8.14/7.66 | 31.83/27.23 | 90.76 | 62.78/ - |   
| PASTA (multi-task) | 96.64/85.09 | 96.42/95.84 | 95.28 | 99.60/99.57 |

Reviewer comment (Presentation):
Appendix section A is empty in the submission. Please remove that in the updated version.

Author response:
**Q5: Response to other questions.**
 
We appreciate the reviewer for pointing out typos in our paper and other suggestions for the presentation. We will follow these suggestions and make revisions in the next version.

Reviewer comment (Presentation):
Citations and links are coloured. They should be black for ICLR.

Author response:
**Q5: Response to other questions.**
 
We appreciate the reviewer for pointing out typos in our paper and other suggestions for the presentation. We will follow these suggestions and make revisions in the next version.