On the Limitations of Reference-Free Evaluations of Generated Text (2022)

Abstract: There is significant interest in developing evaluation metrics which accurately estimate the quality of generated text without the aid of a human-written reference text, which can be time consuming and expensive to collect or entirely unavailable in online applications. However, in this work, we demonstrate that these reference-free metrics are inherently biased and limited in their ability to evaluate generated text, and we argue that they should not be used to measure progress on tasks like machine translation or summarization. We show how reference-free metrics are equivalent to using one generation model to evaluate another, which has several limitations: (1) the metrics can be optimized at test time to find the approximate best-possible output, (2) they are inherently biased toward models which are more similar to their own, and (3) they can be biased against higher-quality outputs, including those written by humans. Therefore, we recommend that reference-free metrics should be used as diagnostic tools for analyzing and understanding model behavior instead of measures of how well models perform a task, in which the goal is to achieve as high of a score as possible.

## introduction automatically evaluating the quality of generated texts is essential for the development of natural language generation systems. the most common type of evaluation for generation tasks such as machine translation (mt) and summarization is done with reference-based automatic metrics, which evaluate a text by comparing it to a gold-standard reference text, usually written by humans @xcite @xcite ., 1 https://cogcomp.seas.upenn.edu/page/ publication_view/991 * work done while at the university of pennsylvania prism-src (↑) source doch er ist nicht krank, er hat nur einen mächtigen kater. reference but he is not ill, he only has quite a hangover. -1.6 candidate but he is not sick, he has only one powerful cat. -0.4 source und mit mann und maus gegen mainz verteidigt. ## reference-free metrics as models conditional text generation models can be viewed as a function θ(•) which scores an output text @xmath0. then θ(•) is used in conjunction with an inference procedure f θ (•) to find the best output at test time. foot_1 θ(x, y) → r (1) for instance, θ(•) could be a learned sequence-tosequence model and f θ (•) could be beam search. the output of f θ (•), denoted ŷ, is typically evaluated by some automatic metric m. referencebased metrics do this by scoring ŷ using some gold-standard text y * (which is not available to the model during inference) and the input x (which is not always used). for instance, m ref-based could calculate a bleu score @xcite between the output translation ŷ and the gold translation y * . in contrast, reference-free metrics calculate a score for ŷ without y * : such metrics include the three analyzed in this work, namely, prism-src @xcite , comet-qe @xcite , and quest-eval @xcite . because θ(•) and m ref-free are both functions of only x and y (equivalently ŷ), m ref-free itself can be viewed as a conditional generation model. for some metrics, such as prism-src, this is explicitly stated, whereas others are implicitly making this assumption. this is not the case for referencebased metrics since they additionally require y * as input. since reference-free metrics are equivalent to generation models, there must exist some inference procedure which finds the best output text under the metric, however, the inference procedure does always exist, and will return the best possible output according to the reference-free metric by definition. we explore the implications of using a model to evaluate other models by analyzing the behavior of three different reference-free evaluation metrics on two text generation tasks, mt and summarization. ## analysis setup here, we discuss the datasets and metrics used in our analysis of reference-free metrics. ## metric optimization since reference-free metrics are equivalent to models, then it is possible to define inference procedures which produce the best-possible outputs according to the metrics. here, we discuss three such (approximate) inference procedures. importantly, they can all be run at test time because they do not rely on a reference text. ## related work various other reference-free metrics have been proposed for mt @xcite , summarization @xcite @xcite @xcite , dialog generation @xcite , image captioning @xcite , and simplification @xcite . reference-free metrics for mt were evaluated and compared to reference-based metrics in the wmt'21 metrics shared task @xcite . in some evaluation settings, reference-free metrics perform better than reference-based metrics. @xcite demonstrate that referencefree metrics evaluate text using undesirable, spurious correlations and propose methods for avoiding learning from those signals. while many of the work which propose reference-free metrics do not explicitly state that they are trying to replace reference-based metrics, we worry that this goal is implied and that the limitations of their metrics are not stated clearly enough. for example, simply by arguing that their reference-free metrics have stronger correlations to human judgments than their reference-based counterparts, an uninformed reader may conclude that the two types of metrics are interchangeable and use a reference-free metric instead of a referencebased metric without fully understanding the consequences. we recommend authors be explicit about the limitations of reference-free metrics and say, for example, they can be used to complement existing reference-based evaluations rather than a replacement @xcite . one such application of reference-free metrics which fits our recommendations for how they should be used is the wmt shared task on quality estimation (qe; @xcite . qe metrics are used to flag translations which might require post-editing or to identify "catastrophic errors" in translations, for instance hateful or violent text that was hallucinated by the model, without the aid of a reference. the intended purpose of qe metrics is not to rank and compare mt systems. we argue that the "rebranding" of qe metrics as reference-free metrics by some authors contributes to the notion that they can and should be used the same way that reference-based metrics are and that they are inherently better because they do not require a reference. we recommend that future proposals of methods for evaluating text without references are called qe metrics instead of referencefree metrics to make the distinction more clear. in previous work, prism-src was further explored by @xcite , who experimented with its model capacity, scoring methods, and more. they perform an analysis using pseudo-references similar to ours in §5.3. in their experiment, they show that the cross-bleu score calculated between prism-src's pseudo-reference and the system output does not appear to be correlated to prismsrc's system ranking relative to human-based system ranking. that is, a higher cross-bleu score does not necessarily result in prism-src ranking a system higher than it should. they conclude that their analysis does not show evidence that prismsrc is biased toward outputs which are more similar to its own, which seemingly contradicts our own experiments and results in §5.3. however, we argue that the high correlations between a system's prism-src score and its similarity to the metric's pseudo-reference (measured by reference-free metrics) does demonstrate such a bias exists, but @xcite did not find evidence this bias negatively impacted the rankings of the systems. our experiments in §4.3 leverage reranking to optimize reference-free metrics' scores. reranking in machine translation has a long history @xcite and has been done with reference-free metrics, for instance in concurrent work by @xcite . the aim of our experiment was not to propose a novel technique, but rather to point out that reference-free metrics can be optimized in this way, and doing so may be potentially detrimental. reference-based metrics can also be used for reranking, for example, by using minimum bayes risk (mbr) decoding @xcite @xcite , which can bias the output toward a particular metric. while techniques like mbr ap-proximate the reference-based reward function, if a system is evaluated with a reference-free metric, the exact reference-free reward function can be directly optimized during inference. ## conclusion in this work, we have argued that reference-free metrics are inherently limited in their ability to evaluate generated text. because they are equivalent to generation models, they can be directly optimized at test time, they are biased toward their own models' outputs and outputs from similar models, and they can be biased against higher-quality text, such as human-written references. therefore, we recommend against using reference-free metrics as measures of progress on tasks and instead advocate for them to be used as useful statistics to calculate in order to better understand model behavior.