Spurious Correlations in Reference-Free Evaluation of Text Generation (2022)

Abstract: Model-based, reference-free evaluation metricshave been proposed as a fast and cost-effectiveapproach to evaluate Natural Language Generation(NLG) systems. Despite promising recentresults, we find evidence that reference-freeevaluation metrics of summarization and dialoggeneration may be relying on spuriouscorrelations with measures such as word overlap,perplexity, and length. We further observethat for text summarization, these metrics havehigh error rates when ranking current state-ofthe-art abstractive summarization systems. Wedemonstrate that these errors can be mitigatedby explicitly designing evaluation metrics toavoid spurious features in reference-free evaluation.

## introduction building reliable automated evaluation metrics is a key factor for quick development of better nlg systems. recent work has proposed reference-free evaluation metrics as a way to judge the quality of generated outputs without the need for human references @xcite . many of these reference-free evaluations achieve remarkably high correlations with human evaluations, raising hopes that they may soon become a viable alternative to expensive human evaluations @xcite @xcite @xcite . however, simply looking at correlation with human scores may not be sufficient to determine the efficacy and robustness of an evaluation metric. in our work, we study recently proposed referencefree evaluation metrics of text summarization and dialog generation. we find that it is possible to achieve similar levels of correlation with human judgment, using simple spurious correlates such as word overlap, length, and perplexity. furthermore, we find that the learned metrics have a rela-tively high correlation with the spurious correlates as compared to human scores, which suggests that these metrics may rely heavily on spurious correlations. this may be a potential explanation for the robustness issues that are observed in recent work, despite the seemingly high reported correlations with human judgements @xcite . we further analyze reference-free faithfulness evaluation metrics and show that the reliance on spurious correlations leads to errors in model selection and development. first, we show that word overlap, a spurious correlate for the task, does as well as recently proposed reference-free metrics at system-level ranking. then, we look at rankings amongst systems that are relatively abstractive and faithful, i.e., the current state of the art, and find that these learned metrics perform significantly worse for these systems. this is because word-overlap is not a good measure for ranking these systems in terms of their faithfulness since all of these systems have similarly low word overlap. this suggests that we need metrics that are not overly reliant on word overlap in their faithfulness prediction. finally, we explore whether a simple mitigation strategy of adversarially training a faithfulness evaluation metric to avoid spurious correlates can lead to a more robust metric. we find that our adversarially trained metric performs well at overall pairwise ranking while having a significantly lower correlation with the spurious correlate of word-overlap. crucially, we show that our proposed metric has improved performance in ranking between abstractive and faithful systems, which is a failure mode for existing reference-free faithfulness evaluation metrics. ## reference-free evaluation of text generation we begin by defining the task of reference-free evaluation, as well as the example-level and systems-level evaluation of these metrics. we define a reference-free evaluation metric as a function f (x, y) that can assign a quality score to an output sequence y for a given input sequence x. the goal of a reference-free evaluation metric f (x, y) is to assign high scores to desirable outputs y for some attribute, such as the faithfulness of a summary. measuring the quality of this metric is challenging, and prior work has relied upon correlation to human judgments h(x, y). example-level evaluation: a number of existing reference free evaluations rely upon a procedure which we call example-level human correlations @xcite @xcite , which measures the effectiveness of a metric by computing a pearson or spearman correlation corr p eval (h(x, y), f (x, y)) over some sampled evaluation data p eval (x, y). ## example-level analysis of learned evaluation metrics in this section, we look at example-level spearman correlations with human judgements for referencefree evaluation metrics that have been proposed for summarization and dialog generation. we compare the metrics to spurious correlates such as wordoverlap, length and perplexity, in order to understand whether the metrics can perform better than these simple measures. we also measure to what extent the proposed metrics are correlated with these spurious measures. ## adversarial model in our earlier example-level analysis, we found that learned metrics have higher correlation with spurious correlates than human judgment. we further saw in our system-level analysis that learned metrics for faithfulness are unable to outperform density. one natural question that follows is whether we can build metrics that do well at the systems level by learning representations that rely less on spurious correlates. in order to do this, we train an entailment based model using the synthetically generated data from factcc in an adversarial setup similar to @xcite . in particular, our approach augments the standard faithfulness predictor with a density predictor that tries to predict the density of the sum-mary from the model's internal representation. we use this density predictor as an adversary, and our goal is to predict faithfulness while ensuring that it is difficult to predict density using this same representation. to achieve this, the gradients from the density predictor are reversed, which makes it harder to predict the density from the encoder's representation, and thus makes the faithfulness predictions less reliant on density. the model architecture is shown in figure 5 . we initialize the parameter λ to 0 and gradually increase it to 1, following the schedule detailed in @xcite . we fine-tune a pre-trained electra model @xcite using the transformers library @xcite for this task. we chose electra in order to match the model architecture in dae. since the original factcc metric was fine-tuned on bert, we also fine-tune our own version of factcc on electra (factcc-electra) as an ablation. our adversarially trained model is essentially the same as factcc-electra, but with an additional adversarial head for predicting density. results. we note that the factcc-electra model performs worse than the original factcc, which is consistent with the findings in @xcite . our adversarially trained metric has a significantly lower example-level correlation with density (27.71%), as compared to factcc (59.10%) and dae (76.37%). we find that the adversarial model foot_4 can achieve a significantly better performance than existing learned evaluation metrics in ranking systems within the abstractive faithful (af) group ( ## related work most existing work on assessing the evaluation methodology of evaluation metrics has focused on reference-based evaluation. for example, @xcite take a critical look at the use of example-level correlations to measure referencebased evaluation metrics in machine translation. they show that evaluating these metrics using example-level correlations can be sensitive to the presence of outliers which can lead to false conclusions about a metric's efficacy. furthermore, @xcite show that proper assessment of evaluation metrics is crucial as uninformed use of automated metrics such as bleu can lead to bad deployment decisions. @xcite has shown that automated reference-based evaluation metrics have robustness issues which can cause them to score generated outputs higher than human written outputs. furthermore, @xcite has studied the limitations of referencebased evaluation metrics of text summarization, comparing these metrics across different datasets and application scenarios. in contrast, our work focuses on analyzing learned, reference-free evaluation metrics in summarization and dialog generation, accounting for potential spurious correlates for these evaluation tasks. there has been some recent work comparing existing reference-free evaluation metrics for text summarization and dialog generation. @xcite has measured the efficacy of existing reference-free faithfulness evaluation metrics of summarization on two different summarization datasets relying on example-level correlations. similarly, @xcite has evaluated automated metrics of text summarization across a wide range of datasets. @xcite has proposed a meta-evaluation framework to evaluate the evaluation metrics looking at certain aspects of these metrics such as robustness, sensitivity, high correlation with human scores, etc., and measured existing evaluation metrics across these aspects. @xcite perform a comprehensive study of existing dialog generation metrics across several different datasets and find that the performance of metrics varies widely across datasets. @xcite and @xcite are the most related to our work since they study robustness of these metrics looking at their performance across different datasets. in our work, however, we explicitly study spurious correlations and show that these may potentially be contributing to the robustness issues. we further present initial promising results suggesting that controlling for these spurious correlates may result in more robust evaluation metrics. ## conclusion in conclusion, we study reference-free evaluation metrics for summarization and dialog generation and show that simply looking at overall examplelevel correlation with human judgment paints an incomplete picture of the effectiveness of a metric. in particular, we show that these metrics are unable to do better than simple spurious correlates for the task. we see that this trend carries over in systemlevel ranking for summarization systems, where a spurious correlate for the task performs as well as existing learned evaluation metrics. we find that despite the relatively high overall system-level ranking performance, the learned metrics are not robust to distribution shifts. we show that they fail to properly rank abstractive and (relatively) faithful systems, which is where the current state of the art operates. finally, we train a faithfulness metric that scores the faithfulness of a summary without relying on the spurious overlap correlate. we show that our metric is more robust across distribution shifts and does better at ranking abstractive, faithful summarization systems. we suggest that future work in designing reference-free evaluation metrics should be mindful of the distribution of the evaluation data. in particular, metrics should be assessed across different distributions of systems in order to test for robustness and failure modes. simple spurious correlates can be used as a tool to indicate potential overestimates of the effectiveness of proposed metrics. finally, we highlight the importance of collecting large-scale human evaluation datasets across a wide range of systems, similar to @xcite , to enable more comprehensive analyses of evaluation metrics.