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 survey and assessment 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 evaluation 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 a generated text is factually consistent with respect to a grounding text (frequently referred to as the "input", or the "knowledge"). such automatic methods attract increasing attention @xcite as they enable both better evaluation and better generation models by automatically filtering training data @xcite or by augmenting training data for controlled generation @xcite . while automatically evaluating factual consistency is an active line of work, there is no single agreed-upon meta-evaluation protocol for measuring the quality of such methods, and labeling schemes vary in their granularity. works are usu-ally done in silo, introducing new datasets and methods that target a specific task or domain, such as summarization @xcite @xcite @xcite or dialogue @xcite @xcite . comparing the robustness of such methods across tasks and datasets is therefore difficult, impeding progress on this subject. in this work, we present true: a comprehensive survey and assessment of factual consistency evaluation methods, covering various metrics, tasks and datasets. we consolidate 11 existing datasets annotated for factual consistency into a unified format, including pairs of a target text and a grounding source text, with a binary annotation of whether the target text is factually consistent w.r.t its source. true foot_1 covers summarization, knowledge-grounded dialogue, paraphrasing and fact verification. 3 the proposed standardization enables us to properly compare consistency evaluation methods in a robust manner across these various tasks and domains. previous works on automatic factual consistency evaluation have mainly focused on measuring system-level correlations of the proposed metrics with human judgements @xcite ). yet, these correlations are not useful for estimating the performance of a measured metric when making example-level, binary decisions, decoupled from specific system implementations (see recent discussion by @xcite on the limitations of reporting correlations). instead, we aim to measure how well a method detects inconsistent texts (recall) and how often it falsely disregards consistent texts (precision), which can be easily computed using the aforementioned binary labeling scheme. therefore, as a meta-evaluation protocol we report the area under the roc curve (roc auc) with respect to inconsistent example detection for each evaluation metric and dataset. our thorough survey and assessment of 12 metrics draws a clearer picture on the state of evaluating factual consistency. we show that natural language inference (nli) approaches, as well as question generation and answering (qg-qa) ap-proaches achieve significantly better foot_3 results on a wide variety of tasks and datasets. we also show that nli and qg-qa are complementary: combining the two yields even better results and hints at room for further improvement. finally, we perform both quantitative and qualitative analysis of our results, finding that all approaches struggle with long inputs, labeling issues and personal statementspaving interesting avenues for future work. to summarize, our contributions are as follows: (1) we argue that work on factual consistency evaluation should be unified and generalized across tasks, and standardize 11 published datasets into a single labeling scheme to corroborate this. (2) we propose a meta-evaluation protocol that allows more actionable and interpretable quality measures than previously reported correlations. (3) we survey and evaluate 12 diverse metrics in this unified perspective, showing that large-scale nli and qg-qa-based approaches achieve strong and complementary results across tasks. (4) we analyze our results both qualitatively and quantitatively, pointing at challenges like long inputs and personal statements to be addressed in future work. ## 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 to measure factual consistency. this comparison should draw a clear picture of current research on this subject and raise directions for future work. for example, we expect that robust metrics should perform well across various tasks and datasets. we next describe the different metrics we assess as part of this study. we note that for all reference-based metrics, we use the grounding text as the reference. for metrics where the scores are not in the [0,1] range, we normalize the scores to be in that range. ## results we report the roc auc foot_14 of various metrics on the standardized datasets in we keep the f1 score in 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 achieved by q 2 and 3 by the anli-based model. 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 18 (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 80 on average on the 11 datasets. this corroborates our hypothesis that 18 pairwise ensembles are reported in the appendix, ## 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 19 and split examples into 6 bins according to the grounding length. 20 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 (with @xmath2.825) as they perform end-to-end qa and nli on text with more than 500 tokens. ## related work adding to the related work mentioned throughout the paper, works on unified evaluation of text grounding generated text explanation the word "philately" is the english version of the french word "philatélie", coined by georges herpin in 1864. the word philately is actually a french word coined by george herpin. the word philately is an english word based on a french word, but not french. all best metrics misclassified this. french police have interviewed presidential candidate francois fillon and his wife penelope over claims she was paid for fake work. they provided information that would help find the "truth", mr fillon said. . . french presidential candidate francois fillon has said he and his wife penelope have been questioned by police over claims she worked illegally. most details are correct and the hallucination is subtle. in the case of q 2 , most of the generated questions have the same answer based on the grounding and the generated text, therefore the overall score was high. stamp collecting is generally accepted as one of the areas that make up the wider subject of philately, which is the study of stamps. i've never heard of stamps, but i do know that the word "philately" refers to the study of stamps. the personal statement "i've never heard of stamps" is not factual and should not be evaluated. evidence suggests that cognitive behavioral therapy and a gradual increase in activity suited to individual capacity can be beneficial in some cases. it has been suggested that cognitive behavioral therapy and gradual increase in exercise could help in some cases so i'm going to try that for now. similar to the previous examples -summac and anli falsely marked the text as inconsistent, probably due to the personal statement. ## 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 and the datasets in true when evaluating 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. future work may also address the challenges of long inputs and personal statements in dialogue, which we point out in our analysis. ## conclusions we presented true, a survey and assessment of automatic factual consistency evaluation methods. we standardized various datasets from diverse tasks into a unified labeling scheme to perform a thorough comparison of automatic evaluation methods, showing that large scale nli and qg-qa based approaches perform well across multiple tasks and datasets. we further show that 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 factuallyconsistent nlp applications.