Natural Language Counterfactual Explanations in Financial Text Classification: A Comparison of Generators and Evaluation Metrics (2025)

Abstract: The use of large language model (LLM) classifiers in finance and other high-stakes domains calls for a high level of trustworthiness and explainability. We focus on counterfactual explanations (CE), a form of explainable AI that explains a model’s output by proposing an alternative to the original input that changes the classification. We use three types of CE generators for LLM classifiers and assess the quality of their explanations on a recent dataset consisting of central bank communications. We compare the generators using a selection of quantitative and qualitative metrics. Our findings suggest that non-expert and expert evaluators prefer CE methods that apply minimal changes; however, the methods we analyze might not handle the domain-specific vocabulary well enough to generate plausible explanations. We discuss shortcomings in the choice of evaluation metrics in the literature on text CE generators and propose refined definitions of the fluency and plausibility qualitative metrics.

## introduction large language models (llm) usage in specialist fields is growing. one specialist application of llms is the analysis of central bank monetary policy communications. communications allow central banks to address factors such as inflation expectations that influence market growth @xcite . in adjusting their own expectations, market participants closely monitor these communications for any signals that may indicate policy changes. on the other hand, central bankers aim to communicate their policy stance to markets clearly, avoiding confusion in their interpretation-a difficult task considering the highly nuanced nature of these texts @xcite . the policy stance of a central bank can be broadly described as either hawkish (tighter policy) or dovish (looser policy). since the bank's current stance is typically reflected in its communications, researchers have studied the use of llms to automatically classify press releases, meeting minutes, and speeches as hawkish or dovish @xcite . as with any use of black-box models in highstakes domains, it is necessary to provide explainability and trustworthiness of these models. however, explaining predictions of an llm can be difficult, especially when they operate in challenging domains. counterfactual explanations (ce) @xcite aim to explain a classification made by a machine learning model by perturbing the original input to generate a counterfactual that yields some desired model prediction. there are many methods to generate counterfactuals for llm classifiers, but most have been trained and evaluated on generic tasks and datasets @xcite . in addition, the methods' evaluations often rely on imprecise quantitative and qualitative metrics. in this paper, we evaluate ce generators for llms on a task from the financial domain. we contribute to the field by: 1. evaluating several categories of ce generators by comparing them from a quantitative and qualitative perspective, considering opinions from domain experts. 2. showing that the state-of-the-art text counterfactual generators perform poorly on texts from specialist domains. 3. highlighting the need for human evaluation and improving the qualitative text ce evaluation metrics by providing more precise definitions. ## related work with the abundance of text ce techniques proposed in the literature, we consider a wide array of methods for generating text counterfactuals. we split the text ce generators into three categories based on how they produce counterfactual explanations. the first category of generators, which we call llm-assisted generation, contains generators that use another llm as a surrogate model to produce counterfactuals. polyjuice @xcite , for example, uses a gpt-2 model fine-tuned for several counterfactual generation tasks. polyjuice is often used as a baseline generator, including in this work. the second category, latent perturbation and decoding, uses the latent representation of the factual sentence and perturbs it to generate a counterfactual embedding. the counterfactual embedding is then decoded into text. as a representative example for this category, we investigate pplm @xcite , which uses a surrogate attribute model to optimize generation for a target class and a fluency model (ex. gpt-2) to ensure high fluency. in the third category, sequential generation, generators first mask a part of the input text and then fill it with new tokens. in this work, we consider the relitc generator @xcite as a representative example. relitc uses feature attribution to generate token masks. the tokens are then filled in with a conditional masked language model (cmlm) one by one, conditioned on a target class. this three-way split allows us to include the different characteristics of text counterfactual generators encountered in the literature while keeping the evaluation in line with the scope of this work. the evaluation methods used in the literature on text ce generators are often related to the desiderata sought by the authors of the methods. researchers often try to optimize for minimality, aiming for minimal perturbations that yield valid explanations. the size of the perturbations is typically measured using distance metrics, such as edit distance @xcite @xcite @xcite , tree edit distance @xcite @xcite , embedding distance @xcite , or semantic measures of similarity @xcite ). another desideratum is validity, that is the success rate or accuracy of explanations @xcite @xcite @xcite . a third popular choice is the fluency of the ce measured using model perplexity @xcite @xcite . finally, numerous methods try to optimize the plausibility of the counterfactual @xcite @xcite or its adherence to the class conditional distribution. the use of perplexity as a fluency metric has previously been criticized @xcite , and metrics like accuracy or distance lead to adversarial-looking ces @xcite . although commonly used, these metrics might be insufficient for assessing text ces. to address this insufficiency, researchers have occasionally relied on qualitative evaluations performed by humans. for example, human evaluators have been asked to judge the fluency of the ces in numerous studies @xcite @xcite @xcite , frequently described as judging whether a sentence "reads like good english". in other works, humans have been asked to assess the fidelity or content preservation of explanations @xcite @xcite also referred to as plausibility and reasonability @xcite , to evaluate if they fall into the original topic. these qualitative metrics are often not rigorously defined, if they are defined at all. unclear definitions can confuse annotators, leading to incorrect annotations. we mitigate this issue by providing more precise definitions of fluency and plausibility to our evaluators (appendix b) inspired by @xcite and @xcite . ## experiments we use a dataset composed of speeches, meeting minutes, and press conference transcripts from the federal open market committee (fomc) @xcite . the texts are split into 1984 train and 494 test sentences and categorized into 3 classes: dovish, hawkish, and neutral. shah et al. (2023) train a roberta-large classifier on this dataset, which we use in our experiments. the dataset contains 49% neutral, 26.2% dovish, and 24.8% hawkish in the train set, and 49.8% neutral, 27.3% dovish, and 22.9% hawkish in the test set. the median text length is 28 words or 178 characters. for each text in the dataset, we assign a random counterfactual label for which a ce should be generated. we use the three generators, polyjuice, pplm, and relitc, to generate ces. for each generator, we generate several ces, which are then classified by the classifier. to keep the experimental setting close to a possible use case scenario, we limit the number of counterfactual explanations generated per instance-generator pair to 5 ces. as a final explanation, we select the text with the highest classification score if the class matches the assigned target class. otherwise, a random counterfactual is chosen. with this experimental setup, we want to recreate a realistic scenario in which a user generates a perfect result for the perplexity ratio metric is thought to be 1 @xcite . the second and third experiments involve human evaluations. for the first round of evaluations, we have recruited native english speakers via the prolific platform. in this round, we ask the evaluators to judge the fluency of the generated sentences on a scale ranging from 1 (poor) to 5 (good). this experiment allows us to perform a large-scale evaluation of 100 factual sentences, with each sentence receiving 5 evaluations, yielding 1,500 non-expert human evaluations in total across all three generators. in the second round of human evaluations, we ask central bank employees to evaluate a subset of the ces from the first round of evaluations for fluency and plausibility. with this expert evaluation, we aim to understand the properties of ces sought after by experts, as well as the overall quality of these explanations in financial text classification. we provide additional information about the survey in appendix c and release the code and data used in our experiments 1 . ## results and discussion we present the results of the quantitative metrics in although quantitative metrics capture characteristics of different ce generators, we are interested in understanding how emerging patterns relate to human evaluations presented in regarding fluency, experts' and non-experts' gradings are broadly aligned. the highest difference between the average grades in with the exception of distance-based metrics, quantitative metrics do not align with human evaluations for fluency. for example, even though the 2 results are computed for all counterfactuals, including ones that do not succeed at flipping the label. we find no major differences when using only successful ces ( relitc generator receives some of the worst results for the perplexity metrics, it produces the most fluent texts according to both groups of evaluators, while the opposite applies to pplm. concerning plausibility, we find that counterfactuals receive less than sufficient expert ratings. despite relitc producing the most fluent counterfactuals, experts assign the highest plausibility scores to polyjuice. this stems from the relitc's misuse of domain-specific words, as reported in the experts' comments analyzed in section 4.1. even though the expert and non-expert fluency scores are nearly the same and dictate the same hierarchy as the distance metrics, there is little apparent correlation between the qualitative and quantitative results. 4 in summary, our findings indicate that many existing quantitative metrics are not reliable indicators for evaluating text counterfactual explanations. ## conclusions in this work, we evaluate a range of text ce generators on a financial dataset. we consider desiderata employed by the authors of the ce generators and aim to answer what qualities of these generators are the most sought after when applied to the financial domain. secondly, we analyze a range of evaluation metrics used in the field and highlight their possible shortcomings. we conduct three experiments, one with quantitative metrics and two with qualitative metrics, involving human evaluators. our findings suggest that methods that apply minimal changes create counterfactuals that are more fluent than those that focus solely on ce validity. however, the plausibility of these explanations is often low. with additional comments from domain experts, we find that an incorrect use of domain-specific terms can diminish the plausibility of the explanations. surprisingly, using ce generators that do not use specialist words might be preferable in specialist domains, suggesting that faithfulness can be as important as plausibility. a secondary finding is that ce generators that perform well on general tasks but do not take into account the classifier or the domainspecific vocabulary might fail when applied to specialist domains. thus, we also recommend future work to evaluate text counterfactuals on non-trivial specialist tasks. additionally, we analyze a range of quantitative metrics used to evaluate ce generators in nlp. we highlight the limitations of these metrics and urge researchers to consider human evaluation when comparing ce generation methods. we find that most of the metrics do not quantify the generators' desiderata well and that they rarely agree with the expert ratings. similarly to recent work on operationalizing algorithmic recourse and ces (buszydlik et al., 2024), we find that there is often no way around the involvement of end users in evaluating ce generators. we emphasize the need to use human annotation when evaluating text ces and provide more precise qualitative metric definitions.