Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: The authors of this paper introduce dynaboard, an evaluation service framework that is integrated with the dynabench platform. The goal of this service is to evaluate and benchmark the progress of NLP models whilst having a focus on reproducibility and accessibility. Dyanboard allows the models to be evaluated directly in the cloud on multiple metrics, including a focus on memory usage. To compare and rank different models, the authors compute dynascore. Further, the evaluation platform provides flexibility to the end-user to customize the dynascore based on weights. 
main_review: Strengths & Weaknesses:
1. The authors tackle an important problem of benchmarking across multiple NLP tasks by introducing a new evaluation service called dynaboard with different objectives compared to existing benchmarks that exist. Similar to existing benchmarks, dynaboard also focuses on reproducibility and accessibility that enables the models to be available. A differentiating factor compared to existing benchmarks might be the ability of dynabench to automatically evaluate a model on a new dataset or metrics.

2.  From a metrics perspective, the platforms provide the flexibility of choosing multiple task-based metrics that are aimed at not only evaluating performance but other aspects including throughput, memory, and fairness. I appreciate the authors for looking at other metrics that are focused on carbon print and memory. However, there seems to be a lack of details in terms of how they are calculated pertaining to throughput, memory. With regards to fairness, just using perturbations is not the right approach for measuring it. This may lead to false validations about the overall performance of a model. Similar concerns are applicable to the metric of robustness.

3. One of the main contributions from this work, is the calculation of the dynascore based on theory from microeconomics. From Table 2, it can be seen that the new scoring approach captures the overall performance of the system and rankings are pretty similar to SuperGLUE.  However, I feel that the main advantage of this new scoring would be reflected in its ability to capture the performance of the system over time and adjust the scoring based on new metrics. 

Questions:
1. What is the level of integration between Dynabench and dynaboard? Are the models submitted to dynaboard evaluated periodically on every new test set from dynabench?
2. With regards to the design choices for the front end, is there any reasoning as to why the users were given the flexibility to pick and choose different metrics? From an evaluation consistency perspective, wouldn't it be better if all the models had some amount of default metrics (metrics for the task )and the rest of the metrics such as memory, throughput, fairness, and robustness can be optional?
3. With regards to the MRS calculation that is dependent on the performance metrics, the current equation does not take into consideration when the performance of two models are the same? Would it be better to include another parameter (Small weight) to avoid this issue?
limitations_and_societal_impact: Yes, the authors have acknowledged some of the limitations of their approach particularly with regards to fairness and robustness.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: This paper presents a platform called Dynaboard for evaluating NLP models in a cloud server and hosting a leaderboard of submitted models. The motive of this web platform is to evaluate models on multiple metrics for performance, memory use and throughput, robustness and fairness metrics for models. Since evaluation is performed on the cloud, the platform also aims to alleviate concerns about reproducibility, accessibility and backwards/forwards compatibility. Finally, users can rank models according to an aggregated metric called Dynascore, where the aggregation criteria is determined by them. The method of computing this aggregated score is based on economic theory, representing each metric as a good, where having more of a good is more beneficial. 

To compute the Dynascore measure, the authors use the idea of indifference curves, which is a set of points (models) that provide the same utility. From this curve, they compute the average marginal rate of substitution, which is the rate at which model creators are willing to tradeoff a metric for a 1-point increase in performance. 

Finally, the paper presents an evaluation of multiple models on 4 NLP tasks on performance, throughput and other metrics included in Dynaboard, along with their computed Dynascore. The authors find that the rankings obtained using Dynascore are more intuitive than the average z-score and that they largely preserve the rankings on the SuperGLUE leaderboard.

main_review: First, the reason for building this platform is well-motivated and targets some very important problems such as reproducibility, accessibility and compatibility. Providing evaluation-as-a-service clearly has its advantages and I think this paradigm shift in evaluation is imperative. The paper is written well, provides a clear description of the motives behind this platform, and describes one method for how metrics can be aggregated using economic theory to reflect the user's needs. I did not learn a lot from the results in Table 2; perhaps the way in which these results are presented can be revisited. I do have several questions and a few concerns:

- Black box evaluation of robustness / fairness: Currently, outputting a single number for fairness and robustness makes these concepts very much like a black box when they really shouldn't be that way. It would be great if these measures could be tied to more real success/failure modes of the model, either by providing examples of where a submitted model fails at being robust / fair or documenting the harms of using a specific model.

- Incentivization: Certain research labs might not be incentivized to pay attention to some of these metrics, when they are clearly of importance to the community at large. Specifically, how does this platform incentivize researchers to still optimize for non-performance metrics? Put another way, how would this platform ensure that modeling progress is driven not just by performance improvements but also other metrics?

- Robustness / fairness definitions: The current formulations of robustness / fairness clearly have weaknesses, which the authors acknowledge in the paper. I think that if the authors envision this platform to be adapted fairly quickly, then these notions need more input from researchers in the corresponding areas. The current definitions are very limited and this might hinder users from adaption. Also, have you considered providing users the option to pick from between several options of fairness / robustness metrics? To avoid the potential harm of providing a number for fairness / robustness when it's imperfect, the authors could include detailed descriptions in the documentation of the platform (if it doesn't exist already).

- Open-sourcing: What are your plans for making this platform an open-source project? Notions of robustness, throughput etc could definitely use input from researchers in those sub-areas.

- Task ownership: How are task owners determined? Further, do you think that the task of picking the default settings for Dynascore should be democratized as well?

- Model card functionality: Have you considered incorporating the ability for users to submit model cards?

- Significance testing and multiple runs: Would it be possible to enable users to perform significance testing within the platform to compare models which have small differences along any metric? Also, do you provide standard deviations / error bars of multiple model runs with different random seeds?

limitations_and_societal_impact: The authors address the limitations of this work in sec 5.4, and generally communicate that their platform is going to undergo changes over time to incorporate more acceptable definitions of different metrics. The authors also outline the societal impact of this work in the last section of the paper. Specifically, they emphasize the potential adoption of green AI through the use of this platform and highlight some issues due to the current definition of fairness within the platform.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper introduced Dynaboard, an evaluation framework in which models are uploaded to and evaluated in the cloud in a way that is reproducible and standardized. Doing so makes it easier to track other metrics that are often neglected, such as throughput, memory usage, fairness, and robustness. Finally, the paper introduces Dynascore, a measure that aggregates performance across different metrics, the weights of which can be dynamically chosen by the user to reflect their preferences.
main_review: I am generally excited to see new approaches to benchmarking progress, and overall find the proposal of Dynaboard promising. It seems quite original (as the authors mentioned, there have been other attempts at hosting leaderboards or trying to improve reproducibility, but Dynaboard seems to cover various facets of these problems not covered by prior work), well-designed, and addresses an important problem. It would indeed be very valuable to make evaluation of models easier and more reliable, while making users more aware of the various tradeoffs different models make. I don't feel as strongly about the design of Dynascore for aggregating metrics (I would have liked more discussion about why this is particularly good and natural), but it also seems pretty reasonable.

Overall, I think Dynaboard is interesting and potentially quite important, and the paper itself is generally well-written.

Some other comments/feedback:
- One thing I like about Dynaboard is that is (to the best of my knowledge) entirely new is evaluation for users in the cloud. However, how scalable is this as models become increasingly large? For example, if in a few years we have open-source models larger than GPT-3, which would presumably require a huge memory and computational footprint, will you still be able to host this evaluation (for free, I'm assuming)?
- Moreover, there are lots of additional metrics and datasets that I would like to see added, I think it would be stronger if many more were already added. This isn't a dealbreaker because it's deliberately extensible, but this may be important for Dynaboard to gain traction, and it makes the framework more exciting to me because of what it could become in the future than because of what it is currently.

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Update: I have read the authors' response and will keep my score at a 6.
limitations_and_societal_impact: The authors raised concerns about including a fairness metric: "Whether to include any explicit fairness metric was a difficult choice – we acknowledge that we might inadvertently facilitate false or spurious fairness claims, fairness-value hacking, or give people a false sense of fairness." This is also a concern I have, and don't think it's clear that including a bad fairness measure is better than including no fairness measure at all, so I'm not convinced of the author's choice to do so. That said, I don't think the choice is obvious either way, and think their decision is more reasonable given that it can be updated over time. Overall I'm glad they discussed this point in a reasonable amount of detail, because I think it's the sort of design decision that can matter a lot.
review_title: null

final_score: rating: 5: Marginally below the acceptance threshold
review_content: summary: The paper proposes an evaluation platform "DynaBoard" to automatic evaluate the models for different NLP tasks according to 5 metrics: performance, throughput, memory, fairness, and robustness. The scores from these metrics can be aggregated according to the custom preferences of each user. The models can then be ranked according to the aggregated score.
main_review: **Pros**

1. The initiative or rather the attempt to provide a holistic evaluation score of the models will be very useful to the community and especially to people working in the application of NLP algorithms to the end tasks.

2. A uniform evaluation of the approaches would make the results of the papers more trustable and designing experiments more reproducible.


**Cons**:

Major ones:
* Most research codes are just written to obtain the maximum performance on a task and are not optimized for throughput and memory. Due to this, the Dynascore would reflect the memory usage and throughput of an unoptimized implementation and this would make the score more implementation dependent rather than method dependent. 

* It is mentioned that currently the evaluation is done on CPUs. However, this is not scalable for larger models which are often more accurate. In addition, often the test data can be quite long and evaluating the model on CPU would not reflect the true throughput and memory usage if the same evaluation was carried out on GPUs.

Minor ones:
* Currently, the authors select just the pre-trained language models for evaluation. Models specialized to tasks should also be included as part of evaluation.

* Due to the paper's emphasis on benchmarking different aspects of the models performance, I feel this paper is more suited in the Datasets and Benchmarks Tracks of NeurIPS rather than in the main conference track. (https://openreview.net/group?id=NeurIPS.cc/2021/Track/Datasets_and_Benchmarks/Round1)


**Questions for the authors**

1. Do you have plans to include more NLP tasks such as machine translation, open-domain retrieval etc? If so, how will you ensure the fairness and robustness metrics are consistently improved for all the tasks?

2. How do you plan to keep the platform running in a sustainable manner in the future? The growth of models and increase in tasks would require an increasing amount of compute to be spent in evaluation and storage in hosting the models.

Depending on the authors response to the above points, I am willing to increase my scores.
limitations_and_societal_impact: Yes
review_title: null

Meta-review: This work on the Dynaboard benchmark is an important contribution to the community as it touches several important points: (a) evaluate a model more throughly not just accuracy (incentivizing aspects such as greenness and fairness) and (b) make evaluation easier, more accurate, and even forward compatible. I am somewhat torn about the decision whether to accept this work to the main conference. While I share some of the concerns from reviewer YZFF and think that there is little technical "machine learning" content (there's an interesting technical content on computing Dynascore, but it's not really about ML), I recommend Accept with the hope that the paper will spur further progress on evaluation in our field.
Decision: Accept (Poster)