Truth or Twist? Optimal Model Selection for Reliable Label Flipping Evaluation in LLM -based Counterfactuals (2025)

Abstract: Counterfactual examples are widely employed to enhance the performance and robustness of large language models (LLMs) through counterfactual data augmentation (CDA). However, the selection of the judge model used to evaluate label flipping, the primary metric for assessing the validity of generated counterfactuals for CDA, yields inconsistent results. To decipher this, we define four types of relationships between the counterfactual generator and judge models: being the same model, belonging to the same model family, being independent models, and having an distillation relationship. Through extensive experiments involving two state-of-the-art LLM-based methods, three datasets, four generator models, and 15 judge models, complemented by a user study (n = 90), we demonstrate that judge models with an independent, non-fine-tuned relationship to the generator model provide the most reliable label flipping evaluations. Relationships between the generator and judge models, which are closely aligned with the user study for CDA, result in better model performance and robustness. Nevertheless, we find that the gap between the most effective judge models and the results obtained from the user study remains considerably large. This suggests that a fully automated pipeline for CDA may be inadequate and requires human intervention.

## introduction counterfactual examples are minimally altered versions of original inputs that flip the initial label @xcite @xcite . they serve as a valuable approach for cda aimed at improving model robustness and performance @xcite @xcite . we want to emphasize the subtle yet significant distinction between counterfactuals used for explaining model predictions and those used for cda. in the former, the objective is to flip the model's prediction, while the goal of cda is to flip the ground truth label (figure 6 in appendix b). 2 to evaluate the effectiveness and validity of the llm-generated counterfactuals for cda, the label flip rate (lfr) is a common metric of choice @xcite . it is the percentage of valid counterfactuals where the ground truth labels are flipped out of the total number of instances. lfr of counterfactuals can be evaluated using either the same model that generates the counterfactual @xcite or independent models @xcite . the optimal strategy for selecting models to evaluate the ground-truth validity of counterfactuals remains uncertain (figure 1 ). this uncertainty, in turn, hampers efforts to enhance model robustness and performance through cda, as noisy or erroneous labels may degrade model performance. in this work, we first define four types of relationships between the counterfactual generator model and the judge model: being the same model, independent models with and without fine-tuning on the target dataset, distilled models, and models from the same family (figure 1 ). secondly, we conduct comprehensive experiments to predict labels for counterfactuals generated by two stateof-the-art approaches across three datasets and four generator models, with 15 judge models. thirdly, we undertake a user study to assess the validity of the generated counterfactuals in acquiring a groundtruth lfr. we find that a judge model with an independent, non-fine-tuned relationship to the generator captures label flipping most effectively. relationships between the generator and judge models that are most aligned with the user study, lead to improved model performance and robustness. additionally, there remains a considerable gap between the performance of the best judge models and the results observed in the user study. ## results judge model performance depends on its relationship to the generator. as shown in figure 3a and figure 3b , llm j with r imwo relationship achieves the highest alignment with human annotators (@xmath0.15, r m/@xmath1.5, ρ = 0.47). in contrast, r dm (@xmath2.58, r m/@xmath3, ρ = 0.38) or r imw (@xmath4.86, r m/@xmath5.23, ρ = 0.29) demonstrate poor alignment with human judgments. this can be attributed to data contamination figure 3 : average ranking and most-to-least ratio for the relationships r g,j : same, distilled, same family, ensemble, and independent w/ and w/o fine-tuning. @xcite , as these models are either finetuned on the target dataset or share architectural similarities with llm g . such overlap could bias llm j in its evaluation of label flipping, potentially leading to either overestimating or underestimating the lfr. notably, ensembling results from all judge models does not necessarily lead to better alignment, as it partially relies on results from suboptimal judge models. additionally, we find that the first two measures (average ranking and r m/ℓ ) are moderately correlated, with a spearman correlation coefficient of 0.67, indicating that r m/ℓ also serves as a reliable metric to capture the alignment between model pairs and human judgments. high downstream performance does not necessarily indicate an effective judge model. despite llm j with r dm or r imw relationships achieving the highest downstream task performance across the target datasets ( identifying label flipping remains highly challenging. llms may struggle to capture nuanced changes when determining the label of imperfect counterfactuals, as the context may not have fully shifted to support a different label, resulting in ambiguity (figure 1 , appendix f). notably, even the optimal judge model fails to fully capture label flipping, exhibiting an average discrepancy of 22.78% relative to the user study results ( relationship r(g, j ) impacts cda outcomes. we investigate how the choice of relationship affects cda outcomes (appendix h). we notice that when the lfr of llm j , due to its relationship to llm g , aligns more closely with user study outcomes, the labels identified by llm j are associated with improved performance and greater robustness on both unseen and out-of-distribution data @xcite (appendix h). this association is particularly evident when these identified labels are treated as the ground-truth labels for counterfactuals, which subsequently serve as data points for cda. this can be attributed to the fact that noisy and incorrect labels provided by judge models with suboptimal relationships contribute to performance deterioration @xcite . ## conclusion in this work, we emphasize the importance of the relationship between the counterfactual generator model and label flipping judge model in achieving lfr that align more closely with human annotations and in improving cda outcomes. we further demonstrate that high downstream performance does not necessarily imply an effective judge model. through extensive experiments, we identify that label flipping remains highly challenging across all selected tasks. additionally, the gap between the optimal relationship and the user study is considerably large, which indicates full automation of cda falls short and human intervention should be considered.