What’s the Meaning of Superhuman Performance in Today’s NLU ? (2023)

Abstract: In the last five years, there has been a significant focus in Natural Language Processing (NLP) on developing larger Pretrained Language Models (PLMs) and introducing benchmarks such as SuperGLUE and SQuAD to measure their abilities in language understanding, reasoning, and reading comprehension. These PLMs have achieved impressive results on these benchmarks, even surpassing human performance in some cases. This has led to claims of superhuman capabilities and the provocative idea that certain tasks have been solved. In this position paper, we take a critical look at these claims and ask whether PLMs truly have superhuman abilities and what the current benchmarks are really evaluating. We show that these benchmarks have serious limitations affecting the comparison between humans and PLMs and provide recommendations for fairer and more transparent benchmarks.

## introduction in recent years, research in the field of natural language processing (nlp) has been driven by a frantic race to reach the top spot in popular benchmarks @xcite @xcite @xcite . typically the race takes the shape of a rapid cycle of parameter tuning updates by several teams, communicating their results using a shared leaderboard. not infrequently, systems achieve better-than-human performance on several tasks (see figure 1 ). yet what does this level of performance really mean for nlp? the impressive capabilities of chatgpt make this question even more urgent. figure 1 : difference between the scores obtained by the best-performing systems and humans in various popular nlp benchmarks. the systems outperform humans on 6 out of 8 of the reported benchmarks (best seen in color). it is relatively easy to outperform humans with simple procedural tasks like arithmetic and extreme memory-intensive tasks involving vast amounts of data. but most tasks involving natural language typically require knowledge and inference. do high-performing nlp algorithms really have (super)human capabilities? or are the metrics that deliver these scores suspect? given the impact of claiming superhuman performance, it is important for researchers to understand exactly what is going on. as many in nlp have experienced, the false sense of accomplishment of superhuman performance often leads to an abrupt disappointment when a supposedly superb system is applied to realistic data in a real-world situation. by propounding unrealistic claims, nlp researchers harm themselves and the field as a whole. some problems result from the metrics used to assess systems, which are invariably automated, and the data these metrics employ, which may be skewed in various ways. the metrics might give incomplete or biased reports of performance, or simply not apply in certain situations. other problems arise from the 'boundary parameters' that shape the task, which are usually not adequately reflected in the evaluation metric, very seldom in the kinds of automated metrics used in leaderboards. specifically, the correctness of a task setup and its dataset instances should not be taken for granted. also, humans and machines are often evaluated under different conditions, such as the level and type of knowledge provided to perform the task and the test items used to compute performance. yet other problems result from the nature of leaderboard-based evaluation. despite the obvious benefit of driving development through competition with little human effort, these evaluations typically do not foster understanding. teams driven by a rapid evaluation turnaround cycle in a competitive mode tend to focus more on quantitative results than on error analyses which aim at improving awareness of their problem. as currently set up, benchmarks and comparisons do not incentivize a deeper understanding of the systems' performance, nor do they foster research geared towards producing automatic explanations: it is one thing to produce a numerical system performance score, but quite another to rate the adequacy and understandability of an explanation. in this paper, we explore the interpretation of the superhuman performance and the utility of leaderboards, discuss how human performance is actually computed in a range of tasks, and how requirements differ for humans and automatic systems across tasks. we hope to encourage leaderboard creators to be more circumspect when setting up their challenges and provide clear 'boundary conditions' and descriptions of the limitations of their evaluations. ## popular leaderboards are saturated leaderboard-based evaluation has become a popular practice in nlp foot_0 @xcite @xcite @xcite . the goal of these leaderboards is to encourage the development of systems capable of solving certain tasks and to measure their progress by comparing the best systems against humans. their great success has led many researchers to focus on just the proposed tasks, resulting in a rapid saturation of the scores which, in many tasks, are equal to or greater than those obtained by humans. as a consequence, many have attributed superhuman performance to such systems, and some tasks have been deemed solved. however, while systems in some areas of ai are compared with the best possible humans, e.g. ibm deep blue vs. garry kasparov in chess foot_2 or ibm watson vs. ken jennings and brad rutter in the jeopardy! quiz show foot_3 , nlp researchers often naively or vaguely estimate the "human baseline", assuming it is a uniform and accepted term of comparison, an established level that systems need to simply beat. in this section we provide a broad overview of existing nlp benchmarks, with a particular focus on nlu leaderboards where human baselines are outperformed by systems, and then show that the construction of such benchmarks is fraught with inconsistencies. the superglue benchmark @xcite ) is a well-known framework for evaluating research towards general-purpose language understanding models for english. it is a collection of 10 language understanding tasks built on existing public datasets, together with private test data, an evaluation server, and a single-number performance metric. in many tasks, humans are outperformed by the best-scoring systems, often by a large margin, ranking 8th in the current overall leaderboard. likewise, the superglue predecessor, i.e. glue @xcite , was built to measure advances in nlu, and the systems' scores quickly saturated the benchmark, thereby sliding the human baseline down to the 23rd position in the ranking. the race benchmark @xcite was designed specifically for evaluating nlp models on a set of challenging reading comprehension tasks, such as question answering (qa) and text summarization. it consists of a large dataset of more than 28,000 multiple-choice questions, which are drawn from middle and high school problems extracted from english examinations in china. these questions cover a wide range of topics and require the ability to reason, understand context, and make inferences based on the provided text. human baselines rank 21st on the public leaderboard, with a gap of almost 20 points compared to the bestscoring system. similarly, the squad2.0 benchmark @xcite is another popular collection of reading comprehension questions and answers based on wikipedia articles. the questions are created by crowdworkers and the answers are a portion of text from the corresponding article. the peculiar difference of this benchmark compared to squad1.1 @xcite is that some of the questions may not have answers, hence systems are required to learn to abstain as well. again, the human baseline is placed in low positions of the ranking, reaching just the 30th place. another notable, related benchmark is coqa @xcite , a large-scale dataset focused on conversational qa systems. in this task, humans rank 7th, with a gap of 2 points from the top system. quite different results are observed when moving to a cross-lingual scenario or when systems are required to perform mathematical and logical reasoning. in particular, xtreme @xcite ) is a benchmark for cross-lingual transfer evaluation that covers dozens of languages spanning 12 language families, and includes 9 tasks that require reasoning about different levels of syntax or semantics. in this case, the human baselines beat the systems in all tasks, with an overall score 8 points higher than that of the best-performing system. xtreme has been succeeded by xtreme-r @xcite , a more challenging multilingual benchmark that covers 14 language families and includes 10 tasks, and similar results have been observed. furthermore, when systems are evaluated over mathqa @xcite inputs, i.e. mathematical questions in the form of natural language, systems perform poorly compared to humans. indeed, humans achieve an accuracy of 87% while systems only reach 54.2%. since systems are still far from human-level performance in these benchmarks, they are out of the scope of our study. however, the highlighted gaps should encourage further research in these areas. an alternative view on system evaluation is presented by the adversarial evaluation framework @xcite , where the evaluation is performed through an iterative "humanand-model-in-the-loop" annotation process. hu-mans are asked to inspect the model output and produce adversarial examples that target specific model weaknesses. the evaluation target is thus a moving goalpost, as opposed to the static targets of most other benchmarks, which saturate quickly. the dynabench benchmark @xcite embraces this approach, incorporating tasks such as nli, qa, sentiment analysis and hate speech detection. it provides a platform for the annotators to examine model output and create adversarial examples. at the time of writing, most of the tasks within dynabench do not report human performance, however. exceptions include the adversarial visual qa task @xcite , where the proposed adversarial examples are solved by other humans and agreement is computed in terms of accuracy. model performance in this setting falls far below the human performance. using more challenging examples for model evaluation, and possibly subsequent re-training, is an appealing approach, likely to strengthen the models with respect to the aspects that the examples target. the caveat is, however, that special care needs to be taken to avoid loss of generality. the annotation of adversarial examples directly depends on the behavior of the model (or set of models) under consideration; the addition of a large number of adversarial examples will likely change the data distribution by potentially overemphasizing rare events; finally, the annotators may focus on a small number of properties of the model, thus "overfitting" the models. although there are many other popular nlp benchmarks to be investigated, e.g. xglue @xcite and @xcite , we limit our review to those benchmarks in which human performance is provided and that can therefore help us answer the main question of this paper concerning the meaning of superhuman performance. ## human baselines are not reliable as discussed above, many nlu benchmarks are saturated (cf. figure 1 ). here we dive deeper into some of them, identify the reasons for their quick saturation, and discuss whether it is fair to claim superhuman performance of state-of-the-art models. in particular, we study superglue @xcite and squad @xcite , as the representatives for general language understanding and reading comprehension, respectively. ## setups favor misleading comparisons summarizing the above observations, we find four main sources of human-to-system comparison error. these correspond to the following key aspects of the evaluation process: system performance, the evaluation data, the measurement process, and humans themselves. we discuss each in turn. ## humans can explain their answers when performing language tasks, humans are capable of explaining why they provided a given answer. thus, when models are claimed to attain human-level language understanding, we can reasonably expect to be able to elicit explanations from them. this has proven highly challenging, however, which casts further doubts on such claims. why do we need explanations? at the level of an individual problem instance, explanations can help users assess whether to trust a given answer. at the level of a system, they help regulators and the general public to assess whether, or in what contexts, a system is safe to use, e.g. by uncovering unwanted biases or by revealing that the system relies on outdated knowledge. in the context of this paper, explanations can help nlp researchers understand the behaviour of their systems, e.g. to make sure that models are right for the right reasons @xcite , or to uncover some of the shortcuts that the model may have learned @xcite , as discussed in §4.1. indeed, the absence of explanations can lead researchers astray. for example, in a prize-winning paper, @xcite analysed several state-of-the-art qa systems and found that they simply classified the best matching answer using their pre-stored knowledge about each question candidate, without performing any 'reading'. none of the papers in which these qa systems were introduced had considered this possibility. what are the challenges? while the importance of explanations is well-understood, progress has been hampered by various issues. one issue is that the evaluation of system-generated explanations is hard to automate ( §4.3). another issue is that it is not always clear what form the explanations should take. for tasks such as sentiment classification, it may be sufficient to highlight which words from the input text have mostly affected a given prediction. however, for nli and qa, providing informative explanations can be challenging, even for humans. this can be observed by inspecting datasets that include human explanations @xcite @xcite . finally, system-generated explanations are typically not faithful, i.e. they do not necessarily reflect the process used by the model. for instance, @xcite found that models can generate contradictory explanations for a given input. ## recommendations based on the findings of the previous sections, we argue that current claims regarding superhuman performance are not adequately grounded, leading to unjustified hype. here we provide a set of recommendations aimed at making comparisons between humans and machines fairer and more reliable. do not favor machines against humans various actions can be taken to set a level playing field between humans and machines, so as to provide a more realistic sense of their actual performance: ## conclusions we have discussed the distressing tendency of many nlp researchers to claim superhuman performance for their systems, and outlined why such claims are not (yet) grounded. we identified problems with evaluation data, evaluation measures and methodology, system understandability, and the human creation of data, all of which contribute to our conclusion. as a final remark, with this paper we hope to make the reader more suspicious and rigorous when claims about "superhuman" performance are made, and, more importantly, to incentivize benchmark creators to address current limitations and design more solid and transparent benchmarks that will advance our scientific understanding of nlp systems and humans.