Will the Prince Get True Love’s Kiss? On the Model Sensitivity to Gender Perturbation over Fairytale Texts (2025)

Abstract: In this paper, we study whether language models are affected by learned gender stereotypes during the comprehension of stories. Specifically, we investigate how models respond to gender stereotype perturbations through counterfactual data augmentation. Focusing on Question Answering (QA) tasks in fairytales, we modify the FairytaleQA dataset by swapping gendered character information and introducing counterfactual gender stereotypes during training. This allows us to assess model robustness and examine whether learned biases influence story comprehension. Our results show that models exhibit slight performance drops when faced with gender perturbations in the test set, indicating sensitivity to learned stereotypes. However, when fine-tuned on counterfactual training data, models become more robust to anti-stereotypical narratives. Additionally, we conduct a case study demonstrating how incorporating counterfactual anti-stereotype examples can improve inclusivity in downstream applications.

## introduction fairytales, traditionally an oral form of storytelling, are used in various cultures as a way to pass cultural norms and practices down through generations. they are used in the classroom and at home to teach children reading comprehension, story structure, and develop cultural literacy @xcite . however, it is known that there are strong gender biases within fairytales, specifically embodied through stereotypes. included in the cultural norms defined in fairytales are gender roles and expectations; however, while cultural norms have evolved over time, these fairytales have not, yet are still purposed in the same way as decades earlier @xcite . in many of these fairytales, the storyline consists of a brave, strong prince rescuing a distressed, helpless princess with true love's kiss. these stories are filled with gender biases that cast harmful and limiting stereotypes on various demographics. studies have shown that repeatedly presenting children with gender bias stereotypes has a negative impact on their confidence and places limitations on their ability @xcite . in nlp, fairytales are useful for assessing narrative comprehension of models due to the quantity and cultural diversity of fairytales as well as their well-studied use in education. although existing question answering (qa) models perform well on fairytale datasets like fairytaleqa @xcite , we are curious how much these models rely on learned gender stereotypes and if these models will have consistent performance when presented with anti-gender stereotypes. inspired by the literature on gender bias evaluation and mitigation @xcite , we create counterfactual datasets to disrupt any learned gender-biased correlations from the pre-trained language models (lms) and pre-trained large lms (llms). specifically, we conduct our studies on fairytaleqa, a narrative comprehension dataset for children in kindergarten to eighth grade. to create the counterfactual dataset, we use three different approaches for data augmentation, includ-ing rule-based translation, llm rewriting, and llm-assisted rule-based translation. the last two approaches leverage the power of llms to support comprehensive rewriting for any text-domain. these approaches perform gender perturbations by swapping gendered nouns such as queen: king while preserving the ground truth labels. the evaluation experiment initially tests pre-trained lms fine-tuned on the original fairytaleqa dataset. these models are then assessed on counterfactual test data synthesized with different approaches. the results reveal a consistent drop in performance, indicating a learned bias in the pre-trained lms. next, we assess pre-trained lms fine-tuned on the counterfactual fairytaleqa dataset, testing them on the corresponding test data. although there's a slight drop in the original fairytaleqa test set's performance, consistent improvements are observed across counterfactual test sets. this suggests that while the overall model accuracy may decrease marginally, the model is able to robustly handle changes in the character information. furthermore, when these models are fine-tuned on a combined random 50% original and 50% counterfactual fairytaleqa dataset, they outperform models solely fine-tuned on the counterfactual dataset. this demonstrates that fine-tuning on both counterfactual and original data supports both normative and counterfactual gender roles. additionally, we perform a small case study to highlight the benefits of incorporating anti-stereotype examples into datasets in the context of diverse fairytale generation. this study introduces an innovative approach to counterfactual data augmentation, emphasizing more generalizable methods of counterfactual data generation and the importance of including counterfactual examples within a dataset. ## approaches & evaluation we utilize cda to perform gender perturbations for the fairytaleqa dataset. we perturb all pronouns and any gendered word, such as princess or seamstress, to its binary opposite gender, prince and tailor, respectively. these gender perturbations are applied to the story section, the question, and the answer. we assess various approaches to cda to find the most precise and robust approach. the approaches presented vary within those two assessment qualities as rule-based translation allows for more controlled augmentations, and llm rewriting allows for a more robust and expansive dictionary of gender pairs. the fairytaleqa dataset is a narrative comprehension resource designed for students from kindergarten to eight grade. the datatset is a collection of 278 culturally diverse fairytales and with 10,580 questions. the question types are broken down to cover seven narrative elements -setting, character, action, outcome resolution, feeling, causal relationship, prediction -of a story @xcite . examples of questions for each category type can be found in the appendix in ## experiments we used two pre-trained lms as our base models for fine-tuning on fairytaleqa: t5 @xcite and bart @xcite . for both models, we set the number of epochs to 4, the learning rate to 5 × 10 -5 , and the seed to 88 for reproducibility. we chose our hyperparameters and lms based on the prior work done by @xcite in order to attempt to reproduce those results as our base for testing. additionally, we chose to assess only t5 and bart to test sensitivity based on finetuning. this approach is necessary for our task, as these stories are culturally robust and therefore out of domain for even the best llms, as suggested in the discussion of name considerations for cda. in the original work, they chose bart as the backbone of their fine-tuned models since it had the best performance for the qa task using fairytaleqa. in our own analysis, we found that finetuned t5 actually outperformed finetuned bart for the fairytaleqa task, so we additionally used t5. we used the chatgpt api (gpt-3.5-turbo) as our llm model for augmentation since the chatgpt api system is built on the gpt 3.5 turbo architecture (openai, 2023). we assess these models on the original and counterfactual fairytaleqa and report the rouge-l @xcite f1 scores as the model performance and use a pooled t-test to compare the results of varying fine-tuned models. additionally, we had a training/validation/testing split of 8:1:1 that was taken from the original dataset, in which the testing data is unseen by the model at test time. ## discussion question type analysis. we stratify the test set by question type for further analysis. for some examples, the model produced unrelated text for some counterfactual test examples. examples of this behavior are in ## conclusion in this work, we evaluate the story comprehension of language models when exposed to counterfactual gender stereotypes using the counterfactual fairytaleqa dataset, generated through multiple cda approaches (e.g., gpt-3.5 turbo and rulebased methods). as shown in