Real-Time Visual Feedback to Guide Benchmark Creation: A Human-and-Metric-in-the-Loop Workflow (2023)

Abstract: Recent research has shown that language models exploit ‘artifacts’ in benchmarks to solve tasks, rather than truly learning them, leading to inflated model performance. In pursuit of creating better benchmarks, we propose VAIDA, a novel benchmark creation paradigm for NLP, that focuses on guiding crowdworkers, an under-explored facet of addressing benchmark idiosyncrasies. VAIDA facilitates sample correction by providing realtime visual feedback and recommendations to improve sample quality. Our approach is domain, model, task, and metric agnostic, and constitutes a paradigm shift for robust, validated, and dynamic benchmark creation via human-and-metric-in-the-loop workflows. We evaluate via expert review and a user study with NASA TLX. We find that VAIDA decreases effort, frustration, mental, and temporal demands of crowdworkers and analysts, simultaneously increasing the performance of both user groups with a 45.8% decrease in the level of artifacts in created samples. As a by product of our user study, we observe that created samples are adversarial across models, leading to decreases of 31.3% (BERT), 22.5% (RoBERTa), 14.98% (GPT-3 fewshot) in performance.

## introduction researchers invest significant effort to create benchmarks in machine learning, including im-agenet @xcite , squad @xcite , and snli @xcite , as well as to develop models that solve them. can we rely on these benchmarks? a growing body of recent research @xcite @xcite is revealing that models exploit spurious bias/artifacts-unintended correlations between input and output @xcite (e.g. the word 'not' is associated with the label 'contradiction' in natural language inference (nli) @xcite )-instead of the actual underlying features, to solve many popular benchmarks. models, therefore, fail to generalize, and experience drastic performance drops when testing with out-of-distribution (ood) data or adversarial examples @xcite @xcite @xcite @xcite . this begs the question: shouldn't ml researchers consequently focus on creating 'better' datasets rather than developing increasingly complex models on bias-laden benchmarks? deletion of samples based on bias baseline reports-hypothesis-only baseline in nli @xcite )-and mitigation approaches such as aflite @xcite (adversarial filtering which deletes targeted data subsets), @xcite , have the following limitations: (i) data deletion/augmentation and residual learning do not justify the original investment in data creation, and (ii) crowdworkers are not provided adequate feedback to learn what constitutes high-quality data-and so have additional overhead due to the manual effort involved in sample creation/validation. furthermore, @xcite show that biased samples are created even when crowdworkers are provided with an initial set of annotation instructions. one potential solution to these problems is continuous, in situ feedback about artifacts while benchmark data is being created. to our knowledge, there are no approaches that provide real-time artifact identification, feedback, and reconciliation opportunities to data creators, nor guide them on data quality. contributions: (i) we propose vaida (visual analytics for interactively discouraging artifacts), a novel system for benchmark creation that provides continuous visual feedback to data creators in real-time. vaida supports artifact identification @xcite ambiguity and diversity of a dataset's language does the sample contribute new words? sample length @xcite , new words introduced @xcite , jaccard index between n-grams @xcite 2. inter-sample n-gram frequency and relation word/phrase repetition and similarity between samples does the sample contribute new combinations of words and phrases? n-gram overlap @xcite , mean-idf @xcite 3. inter-sample sts syntactic, semantic, and pragmatic sentence parsing how similar is the hypothesis to all other premises or hypotheses? multi-hop reasoning @xcite , similarity and overlap @xcite , diversity @xcite 4. intra-sample word similarity word overlap and similarity within sample statements how similar are all words within a sample? coreference resolution, multi-hop reasoning @xcite , word overlap @xcite 5. intra-sample sts phrase/sentence level overlap within a sample how similar is the hypothesis to the premise? n-gram repetition and overlap @xcite 6. n-gram frequency per label distribution of samples according to annotation is the hypothesis too obvious for the system? logic and calculations @xcite , diversity @xcite , outliers, entropy @xcite 7. inter-split sts optimal similarity between train and test samples is the sample too similar to an existing sample? entity distractors, novel clues @xcite , coverage @xcite and resolution, implicitly educating crowdworkers and analysts on data quality. (ii) we evaluate vaida empirically through expert review and a user study to understand the cognitive workload it imposes. the results indicate that vaida decreases mental demand, temporal demand, effort, and frustration of crowdworkers (31.1%) and analysts (14.3%); it increases their performance by 34.6% and 30.8% respectively, and educates crowdworkers on how to create high-quality samples. overall, we see a 45.8% decrease in the presence of artifacts in created samples. (iii) even though our main goal is to reduce artifacts in samples, we observe that samples created in our user study are adversarial across language models with performance decreases of 31.3% (bert), 22.5% (roberta), and 14.98% (gpt-3 fewshot). ## related work this work sits at the intersection of two primary areas: (1) visual analysis of data quality (higher presence of artifacts indicates lower quality), and (2) development of a novel data collection pipeline. 2 ## modules in this section, we describe vaida's important backend processes. dqi: dqi can be expressed as a quality metric that examines different sources of artifacts in text, by scoring samples along 7 different components. we use dqi in order to demonstrate vaida's ability to cover multiple facets of artifact creation, although vaida is metric agnostic. vaida uses an intuitive traffic signal color coding (@xmath0.e., quality) in samples. hyperparameters for color mapping with respect to dqi component values depend on (i) the application type, and (ii) characteristics of pre-existing samples present in the corpus at the time of new sample creation. 4 for instance, when recreating snli @xcite with vaida, we tune hyperparameters separating the boundary between red, yellow, and green flags on 0.01% of the snli training dataset manually in a supervised manner @xcite . autofix: we propose autofix as a module to help crowdworkers avoid creating bad samples by recommending changes to a sample to improve its quality. the autofix algorithm is explained in figure 1 . given a premise, hypothesis, and dqi values for the hypothesis, autofix sequentially masks each word in the hypothesis and ranks words based on their influence on model output, i.e., their importance. the hypothesis word of highest importance is replaced, to achieve at least moderate quality. dqi hence controls the amount and aspect of changes made by autofix. by incrementally changing the sample, users can understand how and why their sample is being modified and how dqi values are affected. textfooler: from an analyst's perspective, the quality of a submitted sample might be "too low" because (i) the crowdworker might not employ autofix appropriately, or (ii) there is a narrow acceptability range due to the criticality of the application domain, such as in bionlp @xcite . we therefore implement textfooler @xcite to adversarially transform low-quality samples (instead of discarding them), to improve benchmark robustness, and ensure that crowdsourcing effort is not wasted. we initially use aflite (bras et al., 2020), to bin samples into good (retained samples) and bad (filtered samples) splits. using textfooler, we adversarially transform bad split data to flip the label; we revert back to the original label and identify sample artifacts using dqi (see figure 5 ). ## interface design and workflow vaida provides customized interfaces for both crowdworkers and analysts, as shown in figures 2, 3 respectively. 5 we describe vaida's workflow via a case study for sample creation (crowdworker) and review (analyst) in figures 4 , 5 . the crowdworker interface provides instructions (a) to navigate through the panels and how to interpret feedback for created samples. communication links for faqs, and error reporting are also provided (f). crowdworkers can then review the instructions for data creation (b)here, we use the same instructions provided in the original snli crowdsourcing interface (b1). ## evaluation we evaluate vaida's efficacy at providing realtime feedback to educate crowdworkers during benchmark creation using expert review and a user study. we also evaluate model performance (bert @xcite , roberta @xcite , gpt-3 (fewshot) @xcite ) on data created with vaida during the user study. ## discussion and conclusion we propose vaida, a paradigm to address benchmark artifacts, by integrating human-in-the-loop sensemaking with continuous visual feedback. vaida uses several visualization interfaces to analyze quality considerations (based on artifact levels) at multiple granularities. while we do not explicitly address computational quality control or fairness consideration (though some aspects can be targeted by currently integrated metrics), since vaida is extensible to the incorporation of customized backend-metrics, we believe our paradigm can support multi-faceted benchmark evaluation. in our usability evaluation, we see that users report greater satisfaction, and lower difficulty with their work and system experience; this implies possible higher crowdworker retention and engagement. additionally, in our study, we see that users effectively identify and avoid artifact patterns during sample creation. based on our study results, we believe that a minimum of 30 annotators would be needed for large-scale data creation to ensure timely feedback (i.e., sample decision provided within 24 hours) to crowdworkers. based on our secondary study, we believe analysts will exhibit increased performance and maintain satisfaction ratings in full-scale creation, as they will become wellversed in the nuances of bias for the data-creation task they are evaluating, as well as the visualizations being used. this, however, is contingent on restricting the visualization views to display only the 200-300 samples with closest artifact levels to the sample being evaluated. overall, samples created with vaida are found to not only of higher quality than achieved with conventional crowdsourcing, but are also seen to be adversarial across transformer models. this is also maintained across multiple task types-we additionally create storycloze @xcite samples with vaida 9 . this was done independent of the study described in the paper, with 4 crowdworkers creating samples. however, for this, several interface features had to be changed, so we focus on reporting only nli results in the main paper. vaida hence demonstrates a novel, dynamic approach for building benchmarks and mitigating artifacts, and serves as a starting point for the next generation of benchmarks in machine learning.