TRUE : Re-evaluating Factual Consistency Evaluation (2022)

Abstract: Grounded text generation systems often generate text that contains factual inconsistencies, hindering their real-world applicability. Automatic factual consistency evaluation may help alleviate this limitation by accelerating evaluation cycles, filtering inconsistent outputs and augmenting training data. While attracting increasing attention, such evaluation metrics are usually developed and evaluated in silo for a single task or dataset, slowing their adoption. Moreover, previous meta-evaluation protocols focused on system-level correlations with human annotations, which leave the example-level accuracy of such metrics unclear. In this work, we introduce TRUE: a comprehensive study of factual consistency metrics on a standardized collection of existing texts from diverse tasks, manually annotated for factual consistency. Our standardization enables an example-level meta-evaluation protocol that is more actionable and interpretable than previously reported correlations, yielding clearer quality measures. Across diverse state-of-the-art metrics and 11 datasets we find that large-scale NLI and question generation-and-answering-based approaches achieve strong and complementary results. We recommend those methods as a starting point for model and metric developers, and hope TRUE will foster progress towards even better methods.

## introduction a core issue in deploying text generation models for real-world applications is that they often generate factually inconsistent text with respect to the input they are conditioned on, or even completely "hallucinate" @xcite @xcite as exemplified in to tackle such inconsistencies, one would like to detect them automatically by predicting whether paraphrasing @xcite ## standardizing factual consistency in this section we elaborate on our re-evaluation setup. we first formally define what factual consistency refers to in this work. we then detail the datasets we consider and how we standardize them. finally, we discuss the meta-evaluation protocol we propose for measuring the performance of evaluation methods on the standardized datasets. ## evaluation metrics we compare various standard as well as state-ofthe-art approaches that measure factual consistency. this comparison should draw a clear picture of current research on this subject and directions for future work. for example, we expect that robust metrics should perform well across tasks and datasets. we next describe the different metrics tested as part of this study. we note that for all reference-based metrics, the grounding text serves as the reference. for metrics where the scores are not in the [0,1] range, we normalize scores to be in that range. ## results we report the roc auc 13 of various metrics on the standardized datasets in the results show that the nli-based models (anli, sc zs ) outperformed the other approaches on 6 datasets, with average auc of 81.5 and 81.4 for anli and sc zs , respectively. q 2 outperform the other approaches on 4 datasets, with an average auc of 80.7. the next best method, bartscore, had lower average auc of 72.2. all other approaches scored 72 or lower on average across all datasets (excluding fever and vitaminc). one outlier is begin, which is the only dataset where simple metrics like f1 token overlap achieved scores higher than 80. we measured the average overlap between the grounding and target texts per dataset, and found that begin exhibits a 12 https://github.com/thomasscialom/ questeval 13 multiplied by 100 for better readability. 14 auc and accuracy for the test sets are provided in tables 10 and 11 in the appendix. we follow @xcite and perform significance testing through bootstrap resampling @xcite , comparing the best method to the second-best method on each dataset. we perform interval comparison at @xmath0.05 and @xmath1.01 and find significantly best results on 6 datasets, 3 from q 2 and 3 from anli. given that no single method outperformed the rest on all datasets, we hypothesize that the nli and qg-qa based metrics are complementary. we test this by averaging the q 2 , anli and sc zs scores per example 15 (ensemble in our results show that a single metric can do well across all tasks and datasets, with all 3 best metrics scoring higher than 70 on all 11 datasets. this corroborates our hypothesis that evaluating factual consistency can be unified, and we hope such unified perspective will be adopted in future work to accelerate progress on the subject. ## analysis input length. as qa and nli models may struggle with long inputs @xcite @xcite , metrics based on them may fail when handling long text. to study the effect of input length on the metrics performance, we unify all datasets 16 and split examples into 6 bins according to the grounding length. 17 we focus on the grounding as the target texts are usually short (see the results are shown in figure 1 . we find that there is a consistent degradation for texts longer than 200 tokens for all metrics, including sc zs which is designed to better handle long text. we find it surprising that the anli-based model and q 2 still do relatively well on the longest bin as they 15 pairwise ensembles are reported in the appendix, ## related work adding to the related work mentioned throughout the paper, works on unified evaluation of text generation across tasks include gem @xcite , where the focus is on evaluating system outputs and not the factual consistency evaluation methods as in true. beametrics @xcite proposes meta-evaluation protocols across tasks, but does not focus on factual consistency. when discussing consistency ("correctness") they measure correlations, which are not sufficient as mentioned in section 2.3. other works on meta-evaluation of factual consistency across datasets include go-figure @xcite frank @xcite and summac @xcite , however they all focus solely on summarization. to the best of our knowledge, our work is the first to generalize the discussion on evaluating factual consistency across tasks and datasets outside of summarization, and the first to show that large-scale qg-qa and nli are highly complementary -setting stronger baselines for future work than previously published. ## discussion and future work we discuss the main takeaways of the true study, pointing at actionable insights for future work. first, as qg-qa and nli-based methods show better performance than other approaches, especially when combined together, we recommend model developers to use those methods for evaluation when factual consistency is a priority. as for metric developers, we recommend using those methods as baselines when proposing new metrics. we also suggest reporting roc auc rather than correlations, as it is more interpretable and actionable. our proposed binary annotation scheme allows to easily test new metrics across tasks and datasets, which would be useful for future work. finally, we encourage data curators to use the binary annotation scheme, which is inline with the recommendations of @xcite . having said that, we do not rule out more detailed labeling schemes -but rather ask to provide a protocol for converting such labels into the more general binary format. we hope that future work will also address the challenges of long input text and personal statements in dialogue evaluation, which we point out in our analysis. ## conclusions we presented true, a meta-evaluation study for factual consistency. we standardized various datasets from diverse tasks into a unified labeling scheme to perform a thorough analysis of automatic evaluation methods, showing that nli and qg-qa based approaches perform well across multiple tasks and datasets. we further show these methods are highly complementary -hinting at additional headroom for improvement while pointing on current limitations. we hope our results and methodology will encourage a more unified perspective in future work to foster progress towards more factual nlp applications.