Evaluation for Change (2023)

Abstract: Evaluation is the central means for assessing, understanding, and communicating about NLP models. In this position paper, we argue evaluation should be more than that: it is a force for driving change, carrying a sociological and political character beyond its technical dimensions. As a force, evaluation’s power arises from its adoption: under our view, evaluation succeeds when it achieves the desired change in the field. Further, by framing evaluation as a force, we consider how it competes with other forces. Under our analysis, we conjecture that the current trajectory of NLP suggests evaluation’s power is waning, in spite of its potential for realizing more pluralistic ambitions in the field. We conclude by discussing the legitimacy of this power, who acquires this power and how it distributes. Ultimately, we hope the research community will more aggressively harness evaluation to drive change.

## introduction evaluation plays a defining role in nlp research; in fact, evaluation has a very rich history. while this genealogy can be traced in many ways, since this piece (roughly) coincides with the 5 th anniversary of the passing of one nlp's beloved pioneers and the first recipient of the acl lifetime achievement award, we look to aravind joshi's legacy. best known for grammar formalism and discourse (see @xcite , his research journey reflects broader field-wide trends towards evaluation. in early works (e.g. @xcite @xcite , evaluation went entirely unmentioned. yet, over time, aravind's work involved more evaluation (e.g. @xcite , implicitly building new norms for evaluation in grammar formalism and discourse @xcite @xcite . @xcite cites joshi's standards for evaluation in conveying joshi's signature belief in multidisciplinary approaches to human language. joshi's life and 5 decades of scholarship teaches us evaluation is important, but how should we reason about evaluation? here, we present two perspectives that frame evaluation in considerably different ways. under the first account, evaluation is technical in nature, functioning as a lens to study models. the motivation for this lens may depend on the specific evaluation, stakeholder, or both: evaluation may allow us to derive scientific insight. or it can transparently document technology for broader audiences (e.g. practitioners, colleagues in other fields, policymakers, the public). regardless, to determine if an evaluation is successful, under this account, the lens must yield the desired understanding about models. in this work, we argue for a second perspective, which we believe is partially acknowledged but considerably less salient than the first perspective. under our second account, evaluation is political in nature, functioning as a force to drive change. in contrast to the first account, this means evaluation pushes the research community in some direction, possibly referring to a specific social or scientific objective, with the emphasis being on future model development more so than existing models. critically, under this account, to determine if an evaluation is successful, the force must yield the desired change in the community. by separating these two accounts, our goal is neither to suggest they are at odds nor that they are meaningfully separable, but to shed conceptual clarity on the merits of powercentric analysis. in pushing for this position of viewing evaluation as a force, we explore what this force influences, what other forces it competes with, how it accrues power, whether its power is legitimate, and who it empowers. motivated by the growing impact of language technology and our field, the abundant discord on the status quo, and the uncertainty on what lies ahead, we believe evaluation's potential for change presents a vital path forward. ## evaluation as a force if evaluation is a force, what domain does it act upon? and where does its power come from? domain. we will restrict our scope to how evaluation influences nlp research. specifically, evaluation concretizes desired behavior for systems, thereby communicating an objective for model design. this allows for the community to coordinate on goals for modeling research. for this goal-setting to succeed, future research should then go on to make progress on the proposed evaluation. that is, successful evaluation requires that the evaluation be prioritized, redistributing research attention such that it is allocated towards making progress on the evaluation. adoption constructs power. as this suggests, the adoption of an evaluation (by others) generates its power and determines its success. it is in this sense that our account for evaluation success deviates from a purely technical/intrinsic characterization. most evaluations are concrete instantiations of a broader agenda: for these evaluations to be effective, they must shift power, namely towards addressing this agenda and materially making progress. in spite of this, we generally find that evaluations in nlp research do not even mention how adoption will arise, and if evaluation creators will take any overt actions to accelerate adoption. ## competing forces having argued for where evaluation draws power from, how powerful is it? while difficult to state in absolute terms, we instead consider what other forces are in play and how they interact/compete. coexisting forces. nlp research is a fabric stitched through myriad social interactions: conversations with colleagues, talks at conferences, academic twitter, scholarship from adjacent disciplines, and much more. most of these interactions are poorly conceptualized as forces: while they exert influence, they are generally diffuse rather than concentrated and lack strong directionality. for this short-form analysis, we juxtapose evaluation with the force of resources. by resources, we refer to assets like money, compute, and engineering support, choosing to treat them as monolithic (rather than disaggregating) for brevity. language models. given the central position language models occupy in modern nlp, we consider language models as a case study to relate evaluation and resources. our thesis is resources, to a far greater extent than evaluation, dictate research on language models, which more broadly influences nlp research given the pervasive dependence on language models. influential language models have near-exclusively been developed by resource-rich institutions. further, we argue a resource-allocation mindset drives decisionmaking in their development. namely, the use of scaling laws (e.g. @xcite indicates development is framed as an efficient resource allocation problem. evaluation does play a small role here: scaling laws relate resources (x-axis) with evaluated model performance (y-axis). but the evaluation scope is narrow: scaling laws generally center accuracy for a single task (generally upstream language model perplexity/loss), with predictability of this relationship being the principal concern @xcite ). in contrast, evaluation currently does not exert similar influence over language model development. namely, while influential language models are similar in that they were developed by resourcerich institutions, they strikingly differ in the benchmarks they are evaluated on. across all datasets evaluated for across language modeling works at the time, @xcite find that rte is the unique dataset evaluated for in more than 50% of the 32 language modeling works they consider (e.g. gpt-3, gpt-neox, bloom, palm, gopher, opt, glm) foot_0 , with some works sharing no evaluation datasets in common. given this status quo, evaluations currently fail to achieve the widespread adoption required to drive change. 2 contrasting properties. which forces orient nlp research is consequential: different forces profile differently. resources are distributed very unevenly, so resources orienting progress implies a small subset of the community expresses outsized influence in shaping the field's trajectory. further, by the nature of how these resource disparities came to be, these resource-rich actors tend to have specific incentives (e.g. commercial interest) and demographics (e.g. poor diversity), potentially causing them to advance particular agendas (see @xcite . in contrast, we believe evaluation structurally is better equipped to enable broader participation (e.g. big-bench) and, critically, pluralism. different values can be simultaneously foregrounded in evaluations (e.g. helm @xcite highlights values/desiderata such as accuracy, robustness, fairness, uncertainty, and efficiency). for example, insofar as scaling laws drive language model development, greater pluralism would be achieved if scaling laws were studied, fit, and applied for a broader array of evaluation targets than just upstream accuracy/perplexity. ## legitimacy since evaluation accrues power, is this power legitimate? and who does this power distribute to? legitimacy. evaluations are generally built by a small number of researchers, but could orient work across the broader research community. consequently, in arguing for the greater use of evaluation as a means for shifting power, we should question whether this implicitly recommends value imposition: imposing values of the few onto the many. however, recall that evaluation's power derives not from its creation but its adoption. consequently, for this power to emerge requires the consensual action of the early adopters, who choose to use the evaluation. to an extent, this (voluntary) choice suggests that the power of evaluation is generally and, at least, initially legitimate. if the power of evaluation is legitimate, then what does this imply when evaluations are shown to have issues with respect to their validity, reliability, relevance, or appropriateness @xcite @xcite @xcite , inter alia)? here, we recognize that while the initial adoption of an evaluation is in most cases clearly legitimate, the subsequent sustained adoption can be more complicated. in particular, we emphasize that evaluations tend to exhibit inertia: once an evaluation is widely adopted, it is hard for the evaluation to lose this status or for other evaluations to eclipse it (e.g. due to reviewing norms; @xcite , even when there are strong reasons to demote or deprecate the evaluation @xcite . most directly, we point to the strong norms of comparison in nlp, whereby model developers are expected to compare their models to prior models in headto-head comparisons. while generally a useful norm, this does promote a certain conservatism. notably, when prior models (i.e. those that are to be compared to) are not public @xcite or laborious to re-evaluate on new datasets, developers of new models can most easily be compare to old models on the evaluations used in prior work. in this regard, paradigms where evaluations are continuously updated and refreshed (e.g. the evaluation rounds in anli @xcite and versions in helm @xcite ; inherently dynamic evaluations like dynabench @xcite ) more directly ensure the sustained power of specific evaluations is legitimate. ## conclusion evaluation wields power: we believe the community is largely aware of this, yet we foreground this power to understand how evaluation drives change. this perspective leads us to three conclusions: (i) adoption imbues evaluation with its power, (ii) evaluation's power relative to other competing social forces appears to be diminishing, and yet (iii) evaluation has attractive qualities, especially under current conditions, as a force for change relative to other forces with growing power. overall, we hope the community reflects on the mantra "evaluation for change".