Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking
Authors: Zhiyi Ma
Kawin Ethayarajh
Tristan Thrush
Somya Jain
Ledell Yu Wu
Robin Jia
Christopher Potts
Adina Williams
Douwe Kiela
Venue: NeurIPS 2021 Poster (2021)
Keywords: natural language processing
evaluation
benchmarking

Abstract: We introduce Dynaboard, an evaluation-as-a-service framework for hosting benchmarks and conducting holistic model comparison, integrated with the Dynabench platform. Our platform evaluates NLP models directly instead of relying on self-reported metrics or predictions on a single dataset. Under this paradigm, models are submitted to be evaluated in the cloud, circumventing the issues of reproducibility, accessibility, and backwards compatibility that often hinder benchmarking in NLP. This allows users to interact with uploaded models in real time to assess their quality, and permits the collection of additional metrics such as memory use, throughput, and robustness, which -- despite their importance to practitioners -- have traditionally been absent from leaderboards. On each task, models are ranked according to the Dynascore, a novel utility-based aggregation of these statistics, which users can customize to better reflect their preferences, placing more/less weight on a particular axis of evaluation or dataset. As state-of-the-art NLP models push the limits of traditional benchmarks, Dynaboard offers a standardized solution for a more diverse and comprehensive evaluation of model quality.

Dynaboard: An Evaluation-As-A-Seriie Platform for Holistic Next-Generation Benchmarking
Zhiyi Mat*
Kawin Ethayarajh
Tristan Thrush
Somya Jain'
Ledell Wu
Robin Jiat
Christopher Potts
Adina Williams!
Douwe Kiela
Facebook AI; Stanford University
dynabench@ft.com
Abstract
We introduce Dynaboard, an evaluation-al aa--s-----------ee------ framework for hosting bench- marks and conducting holistic model comparison, integrated with the Dynabench platform. Our platform evaluates NLP models directly instead of relying on self- reported metrics or predictions on a single dataset. Under this paradigm, models are submitted to evaluated in the cloud, cireumventing the issues of reproducibil- ity, accessibility, and backwards compatibility that often hinder benchmarking in NLP. This allows their quality, and permits the collection of additional metrics such as memory use, throughput, and robustness, which despite their importance to practitioners have traditionally been absent from leaderboards. On each task, models are ranked according to the Dynascore, novel tuility-based aggregation of these statistics, which users can customize to better reflect their preferences, placing more/less weight on a particular axis of evaluation dataset. As state-of-the-an NLP models push the limits of traditional benchmarks, Dynaboard offers standardized solution for a more diverse and comprehensive evaluation of model quality.
interact with uploaded models in real time assess
Introduction
Benchmarks have been critical to driving progress in AI: they provide standard by which models are measured, they support direct comparisons of different proposals, and they provide clear-cut goals for the research community. This has led to outpouring of new benchmarks designed not only to evaluate models on new tasks, but also to address weaknesses in existing models [5) [4]] and expose artifacts in existing benchmarks 42 us with more realistic picture of how much progress the field has made.
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22 37). These efforts are helping to provide
To date, the metrics by which we assess system performance have received much less systematic attention. Even as the benchmarks have changed, the community has continued to rely heavily on accuracy as the sole primary metric. This gives risc to "leaderboard culture" in which accuracy is the only thing that matters, even in contexts in which other pressures c.g., compactness, fairness, efficiency
51 I7]. Recently, Ethayarajh and Jurafsky [17 provided a microeconomic framing of the problem: leaderboard viewers are consumers of models, and each viewer has their own of preferences: some only about accuracy, others value compactness in addition to accuracy, and so on. Static leaderboards that only rank by model performance thus have scoring function that is misaligned with the preferences of most users. Moreover, merely focusing on a single metric, such as accuracy, limits the scope of possible issues
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35th Conference on Neural Information Processing Systems (NcurIPS 2021).

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even to consider. In other words, focusing on one standard metric, like accuracy, lets researchers off the hook too much on other pressing issues.
Reproducibility. accessibility, and backwards compatibility are important issues as well. There is a reliance on self-reported results with trust guarantees [75], and many state-of-the-art claims famously difficult to reproduce [52). In some cases, the state-of-the without sufficient computational resources and/or technical expertise, and older models are often incomparable to newer ones because they have not been evaluated on the same data.
model cannot be accessed
To begin address these issues, propose Dynaboard. Dynaboard's evaluation-as ass---s------e-- service backend allows models to be submitted directly to be evaluated in the cloud, circumventing issues of reproducibility, accessibility, and backwards compatibility. Older models can be compared to newer ones because any model can be evaluated on demand. Dynaboard supports reasoning about many different metrics not just standard accuracy-style metrics, but also memory use and throughput (i.e., inference speed) on identical hardware, robustness, fairness, and so forth. Building on [[7], we borrow from microeconomic theory to aggregate these metries along with performance into Dynascore that is used to rank models. The frontend of Dynaboard is a dynamic leaderboard that allows users to customize the Dynascore by placing more/less weight on a particular metric or dataset. As the user modifies the weights that govern the Dynascore, models are re-ranked in real time. Users can also directly interact with a model via the platform, receiving real-time model predictions that help them understand model's 's capabilities and limitations. and allowing the community to find challenging examples where the current state of the art still falls short. Together, this allows users to develop and directly express the complex and interrelated values they have for their models.
Although other platforms CodaLab, DAWNBench [0), and ExplainaBoard [39, name few - address some subset of the problems we describe above, Dynaboard is the first to address all of them in single end-to-end system. requires minimal overhead for model creators wishing to submit their model for evaluation, but offers maximal flexibility for users wishing to make fine- grained comparisons between models. As state-of-the-art NLP models push the limits of traditional benchmarks, Dynaboard offers a standardized solution for creating the next generation of benchmarks in manner that allows for the diverse and comprehensive evaluation of model quality.
2 Objectives
Dynaboard aims to address the following issues in our current model evaluation paradigm:
The reliance on self-reported results with no trust guarantees 77 makes many state-of-the-art claims difficult to reproduce [52]. Even the choice of random seed can lead to substantially different results [15). and improvements the state-of-the-an are often not statistically significant | Implementational differences in evaluation metrics can also lead to different scores (54). Given that the test data for static benchmarks is often publicly available, reported results may also be the outcome of overfitting hyperparameters 44.
Reproducibility
Accessibility Whichever model happens to hold the of-tt---f-hhh-rrrt title should be accessible by as many researchers as possible. Democratizing model evaluation is essential to understanding our current weaknesses, for making progress in the long tails of the data distribution, and for collecting new adversarial datasets where the state-of-the fails. Just open sourcing the model weights is insufficient, because even when given trained models, inference-time compute resources may not be readily available in many parts of the world.
Backwards Compatibility Dynamic human-in-the-logg data collection is a great alternative to static benchmarks, but as new evaluation sets are introduced in later rounds, old models become incomparable to new ones, simply because they haven't it been evaluated on the same data. It should be possible to evaluate a model on demand, rather than the model's predictions at a single point in time.
Forwards Compatibility Automated metrics such as BLEU and ROUGE have many known flaws, and creating better automated metrics (e.g., BLEURT 64), BERT (77) an active area of research. However, the current paradigm does not allow for old models to be evaluated on new automated metrics that come later, since relies on reporting from the model creator. our field
comes up with new and better metrics, we should be able to immediately gauge overall model quality.

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Prediction Costs Most leaderboards treat the cost of making predictions as zero, which does not hold in practice; memory use, latency, throughput, and lack of robustness among many other factors have real-world implications in deployment [60] [7]. A highly accurate model may be useless to an embedded systems engineer it is untenably large, for example. For users to be able to make informed decisions about the risks and rewards of using particular model, they need to be given as much information as possible.
Utility Estimation There is no single correct way to rank models: every leaderboard viewer has different set of preferences, as manifested in their utility function [17]. Some users only care about model performance. but others care about memory use, throughput, fairness, and more. A leaderboard should not be overly prescriptive when ranking models - it should provide a default ranking, but ultimately give users the freedom to customize the scoring/ranking function better approximate their utility function.
The evaluation-as-a-See as-v-service backend of Dynaboard addresses the first four of these. By having models submitted directly to the platform, they can be accessed and evaluated on demand on any dataset (see Section 4). The frontend of Dynaboard is dynamic leaderboard that addresses the last two issues: prediction costs and utility estimation. Although the leaderboard has a default ranking, the scoring function is constructed in principled way, borrowing from economic theory to estimate the rates at which users are willing to trade-off prediction costs for performance. More importantly users are given the freedom to manipulate the scoring function better approximate their preferences.
3 Related Work
Evaluation platforms Although other platforms have addressed some subset of the issues in
Section [ they have not addressed all of them in a single end-to-end solution. DAWNBench [T0] reports some prediction costs, such as the time needed to reach a particular accuracy. However, it still relies on self-reported results, meaning that reproducibility, accessibility, and forwards/backwant compatibility are still issues. CodaLat and EvalA make it significantly casier to access models and reproduce stated claims, but automatically evaluating any model on any new dataset or metric is not straightforward, making backwards/forwardd compatibility hard to scale. It would also be difficult to collect prediction costs standard hardware. Explainaboard 39] and Robustness Gym (23 allow for a more nuanced comparison of models, but through the lens of which slices of data a model performs well on and what types of errors it makes. Models are not directly uploaded, however, so prediction costs are not collected and forwards compatibility cannot be automated. We see these approaches as complementary Dynaboard, helping provide a richer view of model performance.
Multi-metric evaluation There have been some attempts to rank models by metrics other than accuracy. Although not explicitly framed in terms of utility, Mieno et al. studied the trade-off users were willing to make between accuracy and latency in speech translation [44]. Some challenge tasks in recent years have required the reporting of prediction costs or have instituted limits on the maximum costs that can be incurred, as in the EfficientQA challenge 227 45 However, such
practices are still few and far between. Our hope is that over time Dynaboard can help lower the overhead required to create multi-metric benchmarks and help them proliferate and diversify, such that every cobboommunity can have its own canonical benchmarks, like GEM 21] for natural language generation, BIG-Beneh"] for language model probing, or GLUE (73 and SuperGLUU (22 for NLU.
Backend: Evaluation-as-a-Seer
The evaluation-as-a-serve pipeline uses a standardized application programming interface (API) that is similar to other model evaluation platforms like EvalAI and Codalab that allow creators to upload models. Researchers submit their models for evaluation in our model evaluation cloud, in which all models are scored in exactly the same way, on the exact same data, within the same (virtual) hardware constraints. Testing models on different datasets, from standard well-known test sets to
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stress tests [4,11 and check lists [59] can be cumbersome we offer evaluation-as- aatentttoonn-----ee-eeee as-vvve (EaaS) for NLP models. We ask model developers to specify model cards [46] to report on and properly document their contributions. In this paradigm, it is no longer possible for people to cherrypick results: the same model instance is evaluated on all evaluation sets.
We take a multi-metric approach, going beyond just accuracy, where models are evaluated on multiple axes of evaluation. The top models can be employed in-the-loop for dynamic adversarial evaluation [. and all uploaded models can be made accessible online so that anyone can "talk to" these models (in corresponding frontend UT) to see how they perform. Models are evaluated on combinations of adversarial and non-adversarial datasets. All statistics are made available to the user, who can rank models by specifying which datasets and which metries should factor into the ranking and in what proportion (see Section | for details).
Importantly, we do not claim have built or discovered the one right approach for model evaluation. The goal of Dynaboard and the Dynabench data collection platform it is built on - is making dynamic as many of the components employed by the current status quo as possible: i) rather than having a single metric, we can have many metrics, with the ability to add more as new automated metrics are conceived; ii) rather than having fixed set of evaluation datasets, we can have many and add more over time; iii) rather than evaluating models only on static datasets, they can be evaluated dynamically in the loop. For this approach to succeed, the ability to re-evaluate older models is essential: when new datasets are introduced. new metrics come out, and as new community preferences emerge, we need to be able to see where we stand with our current state-of-the-are
4.1 Metrics
All models are evaluated along multiple axes using a variety of metrics. The ideal model is not only accurate, but fast, memory-efficient, fair and robust. Many of these properties are associated with trade-offs: a binary classifier that always outputs 1 is very fast and uses little memory, but is very inaccurate. While might incorporate additional metrics in the future one of the advantages of our platform is that we are able to do so the currently reported set of metrics is as follows:
The standard evaluation metric in machine learning is some form of performance, whether it is accuracy, F1, AUROC, BLEU, or something else. The exact performance metric, in our case, is task dependent. One task can have multiple performance metrics, but only one metric is used as the canonical performance metric when ranking models. When metrics are aggregated to rank models, our default weighting places half the weight on the canonical performance metric and splits the remaining half among the others (see Section 5 for details).
Performance
Throughput The amount of inference-time compute required by a given model is defined as the number of examples it can process per second on its instance in our evaluation cloud. For fair comparison, models are deployed on the exact same architecture. Measuring the computational efficiency of model is important for two main reasons. First, a highly accurate model that takes very long time label a single example has limited use in most real-world scenarios. Second, as a field, we need to focus more on Green Al and explicitly account for a model's 's energy/carbon footprint in our model ranking (63, 68 28).
The amount of memory that model requires is given in gigabytes of memory usage, averaged over the duration that the model is running, with measurements taken over a fixed interval. Note that unlike with the other metrics, the goal is to minimize memory use rather than maximizing it. For this reason, before aggregating all the metries into single score for ranking models, we transform "memory used" into "memory saved" by subtracting it from the maximum possible (virtual) memory that can be used (see Section 5 for details).
Memory
Whether to include any explicit faimess metric was a difficult choice we acknowledge that we might inadvertently facilitate false or spurious faimess claims, faimess-value hacking, or give people a false sense of fairness. The research community has a long way to go in terms of developing well-defined concrete fairness measurements. Ultimately, however, we came to the conclusion that fairness is such an important axis of evaluation that we would rather have an imperfect metric than no metric at all: in our view, a multi mettic evaluation framework simply must include fairness as a primary axis.
Fairness. There is no single, best way to measure model fairness 35 50 3

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Table 1: Scoring and evaluation-as-a-servv sevviii datasets used for each of the four Dynabench tasks. Unless otherwise indicated, scoring sets are the respective test sets.
Task
Scoring
Evaluation-as-a-Serviee
NLI
SNLI [S], MNLI 74
matched and mis-
Respective dev sets; HANS 43 NLI stress
matched, ANLI rounds 1-3 49
tests [7
Winogender 62 recast NII
QA
SQuAD dev 57 AdversarialQA 1 (Dyn-
AdversarialQA dev, the 12 dev sets from
abench QA round 1) SST3 666] DynaSent [55
MRQA shared tasks [18]
Sentiment
Respective dev sets; Amazon Reviews 78
lest and dev (10k subsample). Yelp Reviews
(78] test and dev (10k subsample)
Hate speech
Learning From The Worst
Respective dev sets; HateCheck 61
We compute the faimess metric based on demographic parity using a black box evaluation protocol, without assuming any access the model itself beyond getting its predictions. Thus the question becomes: how do we construct or manipulate input data such that we can assess some aspects of a model's 's fairness by inspecting its outputs? Following the precedent set by datasets such as Winogender [62] and WinoBias (79], we perform perturbations of original datasets in two ways: (i) by substituting noun phrases that unambiguously encode gender identity with those that have the same part-of-speech but refer to another identity (e.g., replacing "he" with "they" following [14] [3]
and (ii) by substituting names with others that are statistically predictive (70) of another race or ethnicity (e.g., replacing "James" with "Jamal"). This way, a model is considered more 'fair' if its predictions do not change after perturbation, and considered less fair if they do. More details in Appendix A As the community invents better and better black box faireess evaluation metries, which we hope they will, we can re-evaluate older models on new datasets and new metrics, due to our www.kwawdd-oopaatiii
evaluation paradigm. Note that while we use fairness as a metric, we can and do also report results on fairness datasets [62 as part of our evaluation-ase -------------iieee platform.
Robustness. This is a question of how robust model is to particular set of (mostly demographic) axes, and there are many other factors that can be involved a model's robustness. In order to capture this aspect of model quality, we also conduct black box robustness evaluation fairness. That is, following past work on NLP robustness [30 31, we perturb examples and measure whether a model's prediction changes. Specifically, we use the recently released TextFlin evaluation eevalliioooalluaaionn toolkit [24] to do so (details are provided in Appendix B). Initially, we focus mostly on typographical errors and local paraphrases e.g., a "baaaad restuarant" is not good restaurant but just like for fairness, we fully expect this metric to evolve and improve over time.
similar manner to
4.2 Tasks
Currently, there are leaderboards for four tasks: Natural Language Inference (NLI), Question An- swering (QA), Sentiment Analysis and Hate Speech. We evaluate on a combination of adversarially collected datasets for these four tasks, as well as datasets from other sources. Each task has its own set of task owners who control the default setting, i.e., which datasets are included in the corresponding benchmark and in the evaluation suite, how the datasets are weighted, and how the metries are weighted relative to each other for that particular task. Note that the leaderboard rank only determined based on a subset of the full set of aaaluaiion-s-aa-ervee datasets (see Table Tp.
Frontend: Dynamic Leaderboard
The frontend of Dynaboard aggregates the various metrics into a single score for ranking models. Users can manipulate the parameters of this scoring function to better reflect their preferences.
A static leaderboard's ranking cannot approximate the preferences of the leaderboard viewer, because it cannot consider (e.g. computational) costs. Ethayarajh and Jurafsky [7 frame this shortcoming in economic terms: leaderboard viewer is consumer of models and the benefit they get from model
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is its utility to them each individual has their own utility function. The scoring function implicit in ranking by accuracy or F1 is misaligned with the utility function of most users; for example, an IoT engineer looking to find the fastest and smallest model getting 80% accuracy would be poorly served by a static leaderboard.
In reporting many prediction costs, Dynaboard addresses the first of these problems. But how do we combine these disparate metries into a single score that can be used to rank models? Additionally, how can we allow Dynaboard users to align the scoring function with their own utility function? We propose a method for calculating "exchange rates" between metries that can be used to standardize units across metrics, after which weighted average is taken to get the Dynascore. The user can adjust the default weights - e.g.. our loT engineer can place more emphasis on memory and throughput to approximate their utility function; as they do so, the models will be dynamically re-ranked.
5.1 Background
Each model has k properties that informs its utility- - throughput, memory, accuracy. etc. - which can be thought of as a separate good. By definition, good is something one always wants more of, meaning that "memory saved" is a good but "memory used" is not, for example. When a metric is naturally a good, we transform it into a good by subtracting it from budget cap or the maximum of that metric across all models. For example, subtracting memory used from a maximum available memory of 16 GB gives us "memory saved", which we want to maximize. This standardization allows us to use the same method when calculating the trade-off between any two metrics,
A model is a point in this space of goods, and user's utility function maps each point to amount of utils. An indifference curve is a set of points that provide the same utility and can be thought of as a level curve of the utility function [T. Indifference curves are monotonically if one model is strictly better on all dimensions than another, then the two cannot lie on the same negatively sloped line, because the former will have strictly greater utility. For example, for more accurate model to be on the same curve as a less accurate one, the former may have to use up more memory. For given indifference curve, the rate which this trade-off is made i.e., the negative of its slope at a point- is called the marginal rate of substitution (MRS) [4
seraativel--lopped
5.2 Converting between Metrics
To calculate the default Dynascore, we first estimate the rate at which users are willing to trade-off each metric for a one-point gain in performance (i.e., MRS with respect to performance) and use that to convert all metrics into units of performance. Once converted, a weighted average is taken to get the final score. We choose this approach for two reasons. First, by drawing from the microeconomics literature, is a principled approach to user personalization that builds on the utility- static leaderboards [17]] Second, typical normalization methods (c.g., 2-scores) do not work well when a metric's ' values are highly skewed, as they often are in practice. For example, as seen in Table 2 taking weighted average of 2-scores would lead to T5 ranking worse than the majority bascline on many tasks; this happens because T5's high memory use is as much an outlier as the baseline's s poor accuracy. Even BERT, more lightweight model, ranks worse than the majority baseline on NLI and QA when ranking by the average 2 z-score, which is highly unintuitive.
critique of
Given that each user has their own unique utility function and thus unique indifference curves each has a different rate at which they are willing to make a trade-off between a metric M and the performance metric perf. This is fine; the default "exchange rates" between the metrics should merely be a starting point that can be adjusted by the user as they see fit (see Section 5.3). To calculate the default exchange rates between some metric M and perf, we consider the simplest possible case, where each indifference curve only lies in two dimensions - i..., the utility changes only with respect to M and perf- and all models lie on the same indifference curve. This assumption is needed because we do not have direct access to any k-dimensional utility function; our data is sparse.
We then calculate the average marginal rate of substitution (AMRS) of this indifference curve, which tells us the rate at which model creators, as a group, are trading off M for a one-point increase perf while keeping utility constant, For example, if AMRS of "memory saved" with respect to accuracy were 0.5 GiB, then each GiB of memory saved would on average be worth 2 points of accuracy. By dividing M by AMRS(M, perf), we can convert it to units of performance.

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U
U 10
M
M
MRS
perf
perf
Figure I: Left: The marginal rate of substitution (MRS) is the negative of the slope of an indifference curve (shown in red) with fixed utility U, each of which shows the trade-off being made between metric M and performance perf. Since model (a) and (b) cannot possibly lie on the same curve because the former is better both respects we assume that when all else (including utility) is held constant, the increase in performance from (b) (a) should come at the expense of the increase in M, giving us an estimate of where the higher indifference curve lies. Right: Taking the line-of-best-fit to estimate this trade-off would not work when some models are strictly better than others.
Note that this means that the Dynascore inherently dynamic and incorporates the AMRS at a given point in time, and subject change more models added.
Assumptions What if there were no trade-off between a metric and performance? For example, performance might rise with robustness up a point and come the cost of robustness past that. If the ith most accurate model I; had both better performance and were more robust than the next most accurate model 21+1. then they could not possibly lie on the same indifference curve, since having more of both goods is axiomatically preferable to having less of each. To tackle this problem, we make two assumptions:
(i) Mx;;) > M(x++1) and perf(r;) > perf (I;+1), then models {ıylū i) lie on lower indifference curve than I; though not all necessarily lie on the same one; (ii) there exists model (perf (Zi41). M(x;) + (M(x;) M(x++1))) on the same indifference curve as I; (i.e., I; and 2;41 would be on the same curve if the latter obtained M(z;) M(z;+1) more on metric M). In other words, all else (including utility) held constant, we assume that the increase performance from Fi+1 should come at the expense of the change in M. This is just an estimate, but it provides a good starting point from which users can choose their own AMRS (Section (.3).
These assumptions, visualized in Figure [ allow every model to be represented in the AMRS calculation. Under these assumptions about where the higher indifference curve lies, calculating the MRS is equivalent to taking the absolute value of the slope between points (rather than just taking the negative of the slope
{
M(x,)) M(x++1)
MRS(M, perf)
|
peprf(x.)) - perf(x:+1)
AMRS perf)
-RRS
5.3 Weights and Customization
After converting all the metrics into units of performance, we take a weighted average of the converted values get the Dynascore for model. For model 3;; with normalized weights ux Z:
M(x:)
Dynascore(x:) A Σ WN AMRS (M, perf)
(2)
The default normalized weights are uperr 0.5 and Wyppert 0.5/ 1). In other words. performance makes up half the Dynascore while the remaining half is split equally among the other metries. This is because, in theory, gains in all the other metries may come at the expense
SSinee the MRS is undetined when (erf perf(#;4) is zero, we ignore increases in performance that fall below predetermined threshold epsilon, e-small. We currently set to 1xx x 10-4 for all tasks.
7

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Table 2: Non-dynamic leaderboards for the Dynabench tasks, sorted by the Dynascore. Performance (Perf.) measured by F1. Note that ranking by the average 2-score (with the same metric and dataset weighting as the Dynascore) leads to far more unintuitive ranking than with Dynascore e.g., T5 often ranks worse than the majority baseline because its memory use is such an outlier. Also that although "memory used" is reported, before incorporating it into the Dynascore and 2-score, we subtract from the maximum possible 16GB to get "memory saved" to be consistent and express as utility (i.e., having something we want to maximize).
Task
Model
Throughput
Memory
Faimess
Robustness
Dynascorc
Avg. a-score
DeBERTa
69.54
7.41
5.71
91.97
75.70
38.83
0.24
9.23
ALBERT
67.29
9.60
2.18
89.94
74.12
37.72
0.26
NLT
67.16
7.10
BERT
64.82
9.39
4.13
92.11
66.38
36.36
0.06
Majority Baseline
32.41
77.33
1.15
100.00
100.00
22.78
0.10
FastText
31.29
73.94
2.20
83.23
69.14
21.13
-083
DeBERTa
76.25
4.47
ELECTRA-large
76.07
2.37
25.30
93.13
91,64
45.79
0.33
ReBEKIa
69.67
6.88
6.17
88.32
86.10
42.54
0.27
ALBERT
68.63
6.85
2.54
87.44
80,90
41.74
0.16
QA
BERT
57.14
6.70
5.55
91.45
80.81
36.07
-0.02
Unrestricted TS
28,80
4,51
10.69
92.32
88.41
22.18
-0.52
Return Context
1.10
15.47
DeBERTa RoBERTa
76.07
7.50
4,80
94.08
79.21
71.31
0.34
T5
73.20
7.12
9.06
93.44
77.99
69.32
0.00
ALBERT
70.61
78,44
BERT
68.71
8.83
6.04
93.49
72.75
66.81
-0.07
Majority Baseline
35,93
35.14
1.07
100,00
100,00
57.94
-0.27
FasTText
53.32
32.54
1.69
78.52
65.82
57.39
-0.57
DeBERTa
81.34
6.20
RoBERTa
80.26
6,61
3.67
85,02
79.09
47.77
0.26
ALBERT
76.84
8.01
2.25
84.50
79.98
46.18
0.23
Hate Speech
BERT
76.58
7.26
3.22
86.45
77.35
45.97
0.15
T3
76.59
5.48
10.47
86.71
78.52
45.80
-0.19
Mujority Baseline
54.69
FastText
49.70
15.34
2.61
80.09
71.12
32.46
-0.93
of performance, giving each metric a weight of 1/m would favor degenerate models due to their efficiency and close-to-7ero memory. To avoid this, the weighting was split evenly between performance and the (hypothetical) costs of performance.
Dynaboard user can customize the Dynascore to better approximate their own utility function by adjusting the un-normalized weights. In doing so, they are effectively expressing the average rate which they would be willing trade off metric against performance. That is, using the user-specified normalized weights ZM is equivalent to using the default normalized weights with an effective exchange rate (wx/ (2//2))AB AMRS(M, perf). If Dynaboard user cares more about metric than in our default setting (i.e., uw < 2x), then the adjusted AMRS will decline to reflect the fact that they are less willing sacrifice M for a marginal increase performance. A possible line of future work is studying NLP practitioners' preferences over time and across different niches (e.g., industry vs. academia). Asking such questions has only been made possible by adopting this utility-based framework for Dynascore.
5.4 Limitations & Future Work
In general. the AMRS estimates are better when our assumptions 5..5. need apply i.... when the models can all lie on the same convex indifference curve. However, we stress that the estimates are only starting point and are meant to manipulated by the user by adjusting the weights.
Since every metric is converted into units of performance, if all models perform exactly the same, the AMRS cannot be calculated. The reason we use performance as the base metric is because every task is guaranteed to have some performance metric, while the other metrics may differ substantially across tasks. However, it is possible pick different base metric in principle: could calculate the AMRS of M w.r.t. memory saved, for example, and convert everything into
Identical Models

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GiB. Similarly, the converted value for M will be undefined if all models obtain exactly the same value for this implies that model creators are not willing sacrifice M at all for an improvement in performance and therefore that the metric is infinitely more valuable relative to others. Both these issues are unlikely to occur practice, however.
Threshold Sensitivity We choose to ignore increases in performance less than = 1 x 10-4 so that the MRS is never undefined, which we consider reasonable since almost all existing leaderboards only report to 1 or 2 decimal points, an improvement of 1 X 10 would be indistinguishable to the user. One could reasonably argue that this threshold needs to be higher however, since many NLP datasets are too small for 0.01 or even 0.1 increase to be statistically significant [8]. Changing can change the Dynascore and the model rankings. However, to avoid potentially confusing the user with adaptive thresholds based on statistical power tests, we chose to fix at conservative value.
Human Validation Not all models and tasks lend themselves to be evaluated automatically; some need humans in-the-loop, either by definition over time models improve. In the future, hope to incorporate the validated model error rate (vMER) (34] the number of numan-validated model errors divided by the total number of examples benchmark can use vMER as a base performance metric.
metric on the leaderboard, such that any
5.5
Results
We take models that currently represent the state-of-the-ar on our tasks and put them through our evaluation pipeline. Specifically, we finetune and evaluate BERT [12. RoBERTa [40], ALBERT [36]. T5 [56] and DeBERTa [26] on tasks, with the addition of ELECTRA
for QA. This set of
models roughly encompasses the top 5 models GLUE 73 and SuperGL UE [72]. As baselines, we add FastText [32] for sentiment and hate speech and BiDAF [65 for QA. We also compare to the majority baseline for the classification tasks (using the training set majority) and for QA baseline that simply returns the entire context. These baselines are obviously efficient and robust to perturbations, but have low performance. See Appendix for further details. We compute the Dynascore for each model using the default weights for the metrics and giving equal weight to all datasets. Recall that the Dynascore is dynamic and incorporates the AMRS given point in time, and so is subject to change as more models are added.
The results are reported in Table 2 We find that the SuperGI.UF ranking is roughly preserved, with Transformer models clearly outperform their predecessors and the basclines. Even when we factor in the additional axes of evaluation, DeBERTa still performs best. In some cases, the gap is actually larger: in terms of accuracy on SuperGLUE, DeBERTa outperforms T5 by 1.1%, but on the sentiment task it's 3.1% 1%, while being more efficient, as reflected in the Dynascore. However, there some differences with the SuperGLUE rankings, most notably with T5, which ranks much lower here because of its relatively low throughput and high memory use. It is also worth noting that FastText does consistently worse than the majority baseline, even when it is more accurate, since the gain in accuracy is not enough to offset its sensitivity to fairness and robustness perturbations.
The somewhat old-fashioned medium of a paper does not do justice to a dynamic concept like a leaderboard with specifiable utility function, and linking to the actual implementation would break anonymity, We refer the reviewer to the supplementary material for examples.
Conclusion
We introduced an end-to-end solution for hosting next-generation benchmarks enabling a more holistic and comprehensive evaluation of model quality. Our selluation-asa-seviie platform addresses several important shortcomings in the current status quo, from reproducibility to accessibility to backwards/forwards compatibility. Dynaboard also enables the collection of prediction costs and factors these costs into the overall ranking of models, which users can customize to better align with their own preferences. With NLP models playing an increasingly important role in our daily lives, deciding which model is better than another one is becoming a crucial problem, both for driving further progress and ensuring that we deploy our systems responsibly. We hope that Dynaboard can allow benchmarks to proliferate and diversify, paving the way for the next wave of advances in NLP.

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Acknowledgments and Disclosure of Funding
We thank our collaborators in the Dynabench team, especially Max Bartolo, Yixin Nie and Bertie Vidgen, for helpful comments and suggestions. We're grateful to Amanpreet Singh and Sujit Verma for useful feedback and engineering suggestions. We thank Eric Smith and April Bailey for helping with name lists for the faimess perturbation. We also thank Devi Parikh, Alicia Parrish, Pedro Rodriguez, Dan Jurafsky, Ethan Perez, Kyunghyun Cho and Alex Wang for comments on an earlier draft, and Gargi Ghosh for her support throughout. We thank the TextFlint team for their help on the toolkit. Kawin Ethayarajh was supported by an NSERC PGS-D.
Broader Impact
Green AI & Ethical AT A key barrier to the adoption of Green Al 633 has been the incentive structure in NL.P leaderboard culture, which places emphasis on higher performing (i.e., more accurate) models the detriment of models that are more lightweight and efficient [17]. Although there are exceptions to this - such as the EfficientQA challenge task [45] the dominant paradigm still largely ignores these factors. Changing incentive structures is important because they ultimately shape what kind of problems the NLP community works on and what kind of models they build. By drastically lowering the overhead needed to host multi-metric benchmarks, Dynaboard will hopefully allow benchmarks to keep fulfilling their crucial role in driving research progress, while incentivizing the creation of models that are "greener" and more fair, among other qualities.
Fairness Our perturbation-baser black box fairness evaluation method is an initial attempt at measuring fairness for NLP tasks/models, and we fully expect this to evolve over time as advances are made in the field. Firstly, our method assumes that fair system will be one that treats all genders and races/ethnicities equally. We take equal treatment (treating all groups equally) to be a first step towards a longer term goal of equitable treatment (treating all groups fairly in accordance with their needs or circumstances). The equal treatment perspective implies that fairness perturbations of the input should not affect the classification label. In most cases, this assumption is valid; however, this is not always true, and even when it is, does not necessarily apply equally to all members of the group referenced in the example (e.g., not everyone who identifies woman can or chooses to give birth). This makes our mcasurements noisier. We do not currently use our perturbation method to filter out any examples from our data, because this may unintentionally lead to the over- or under-repressnnatiio of certain topics and voices.
Secondly, whenever one chooses a set of demographics, they are immediately codifying particular categories and deciding which groups to include or leave out. We have chosen for now only a set of size two-gender identity and race/ethnicity. We chose these largely because they have clear and measurable effects in language (i.e., with respect to pronoun morphology, noun phrases, and names). Within these two broad groups, we have chosen to include only a subset of the possible perturbations: man- woman for gender, and Asian -acaffc Islander-Blatd Blacc--llck-Hiie Satack-Hispani-weee white for race/ethnicity). For the latter, this decision was driven by data availability and could reflect US US-centric bias present in the existing datasets. For the former, we intended to additionally include a gender-neutral perturbation, which could swap names that were statistically more likely to refer to men or women to those that were gender-balancee balanced, and similarly to perturb "sister" and "brother" to "sibling". "her" and "him" to "them", and "female" and "male" to the empty string. We feel this direction is of clear importance, since it can make it possible to uncover examples that are unfair to nonbinary people. Unfortunately. there were various idiosyncrasies both in our language ("standard" American English), and in the existing datasets that made the quality of the examples decrease too drastically for us to conclude that swap with
a gender-neutral perturbation would yield useful signal. For example, for the "(fe) the empty string, we observed that 50% of the uses of these terms in MNLI were nouns; permuting them to the empty string would result in ungrammatical examples. Furthermore, in addition to case collapses between possessive forms of the pronouns (i.e., "their(s)",, singular-they has an additional complication that it controls plural verbal agreement, meaning that heuristic perturbations often introduce additional ungrammatical noise above and beyond the case collapses in morphological form). Due to these and other complications, we reluctantly set aside gender- for future work. All this being said, there is ample room to improve upon our current fairness perturbations, not only by extending the scope of included groups, but also by exploring options for perturbation that are more flexible.
perturbations
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Thirdly, our fairness perturbation method might have different consequences for different tasks. For Sentiment Analysis, QA, and NLI, our spot checks suggest that data crucial for the classification is affected only rarely (see Appendix [A] for more discussion). However, for hate speech, the situation is a more nuanced. In data collection [7i]], abuse against men, straight people, white people, etc. was not considered (either as hateful or non-hateful). By performing heuristic perturbations of gender identity and race/ethnicity words, we post hoc include these sorts of examples. Since they have never seen such examples at training, this may overestimate model sensitivity to perturbation.
Finally, we would like to re-emphasize that there are many ways to measure and conceive of fairesss of NLP models. We have chosen to adopt a perspective where the gender and race/ethnicity of entities in examples should not affect classification decisions. We think this is valid for the tasks hosted on Dynabench so far. This being said, models that perform well on our fairness perturbations should not be taken to necessarily be "fair" models, although we do feel it is safe to conclude that any models that perform poorly on these perturbations have room for improvement.
Dynascore As discussed in Section 5 the Dynascore of a model is not fixed. Rather, it dynamic on many levels: (i) as new models are added, the rates at which metrics are converted to units of performance may change; (ii) as the weights change, the model rankings will change. Therefore the default score is not fixed and canonical measure of a model, and model creators should avoid reporting the Dynascore of their model out-of-contett: a Dynascore only has meaning in the context of all the other models on the same leaderboard. However, users may not appreciate these caveats, and they may report the default Dynascore of their model in a research paper, much in the way they would report accuracy or FI. To discourage such behavior, the Dynaboard UI will provide clear warnings to the user, informing them of the limitations of Dynascore and the fairness and robustness metrics. If any Dynascore is reported, recommend that users explicitly report minimally the dataset, the metric weights, and the timestamps.
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[81] Jieyu Zhao, Yichao Zhou, Zeyu Li, Wei Wang. and Kai-Wei Chang. Learning gender-neutral word embeddings. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages
2018
For all authors...
(a) Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope? [Yes]
(b) Did you describe the limitations of your work? [Yes]
(c) Did you discuss any potential negative societal impacts of your work? [Yes]
(d) Have you read the ethics review guidelines and ensured that your paper conforms to them? [Yes]
2. If you are including theoretical results...
(a) Did you state the full set of assumptions of all theoretical results? [N/A]
(b) Did you include complete proofs of all theoretical results? [N/A]
3. If you ran experiments..
(a) Did you include the code, data, and instructions needed to reproduce the main experi- mental results (either in the supplemental material or as URL)? [Yes]
(b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes]
(c) Did you report error bars (e.g., with respect to the random seed after running experi- ments multiple times)? [No] (Users upload only a single model instance)
(d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, cloud provider)? [Yes]
16

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4. If you are using existing assets (e.g., code, data, models) or curating/releasing new assets...
(a) If your work uses existing assets, did you cite the creators? [Yes]
(b) Did you mention the license of the assets? [N/A]
(c) Did you include any new assets either in the supplemental material or as a URL? [Yes]
(d) Did you discuss whether and how consent obtained from people whose data you'n using/curating? [N/A]
(e) Did you discuss whether the data you are using/curating contains personally identifiable information or offensive content? [Yes]
5. If you used crowdsourcing conducted research with human subjects...
(a) Did you include the full text of instructions given to participants and screenshots, if applicable? [N/A]
(b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, applicable? [N/A]
(c) Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation? [N/A]
17


Reviews:
[{"review_id": "sD8274U8pEV", "reviewer": "Reviewer_qZyj", "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. ", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strengths & Weaknesses:\n1. 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.\n\n2.  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.\n\n3. 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. \n\nQuestions:\n1. 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?\n2. 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?\n3. 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?", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Yes, the authors have acknowledged some of the limitations of their approach particularly with regards to fairness and robustness. ", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "30"}, "extra_text": {}}, {"review_id": "jbz1gPaoMPL", "reviewer": "Reviewer_Uxmc", "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.", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "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.\n\nOverall, I think Dynaboard is interesting and potentially quite important, and the paper itself is generally well-written.\n\nSome other comments/feedback:\n- 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)?\n- 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.\n\n------------\nUpdate: I have read the authors' response and will keep my score at a 6.", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "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_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "3"}, "extra_text": {}}, {"review_id": "MJqidPRiRga", "reviewer": "Reviewer_mrnU", "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. \n\nTo 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. \n\nFinally, 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.\n", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "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:\n\n- 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.\n\n- 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?\n\n- 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).\n\n- 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.\n\n- 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?\n\n- Model card functionality: Have you considered incorporating the ability for users to submit model cards?\n\n- 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?\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "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.\n", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "4"}, "extra_text": {}}, {"review_id": "ACVOa4MtloB", "reviewer": "Reviewer_YZFF", "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.", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "**Pros**\n\n1. 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.\n\n2. A uniform evaluation of the approaches would make the results of the papers more trustable and designing experiments more reproducible.\n\n\n**Cons**:\n\nMajor ones:\n* 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. \n\n* 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.\n\nMinor ones:\n* Currently, the authors select just the pre-trained language models for evaluation. Models specialized to tasks should also be included as part of evaluation.\n\n* 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)\n\n\n**Questions for the authors**\n\n1. 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?\n\n2. 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.\n\nDepending on the authors response to the above points, I am willing to increase my scores.", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Yes", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "5 hours"}, "extra_text": {}}]

Author rebuttal:

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)