Recent studies show that traditional fairytales are rife with harmful gender biases. To help mitigate these gender biases in fairytales, this work aims to assess learned biases of language models by evaluating their robustness against gender perturbations. Specifically, we focus on Question Answering (QA) tasks in fairytales. Using counterfactual data augmentation to the FairytaleQA dataset, we evaluate model robustness against swapped gender character information, and then mitigate learned biases by introducing counterfactual gender stereotypes during training time. We additionally introduce a novel approach that utilizes the massive vocabulary of language models to support text genres beyond fairytales. Our experimental results suggest that models are sensitive to gender perturbations, with significant performance drops compared to the original testing set. However, when first fine-tuned on a counterfactual training dataset, models are less sensitive to the later introduced anti-gender stereotyped text.

This paper looks at the ability of large language models to participate in educational guided reading. We specifically, evaluate their ability to generate meaningful questions from the input text, generate diverse questions both in terms of content coverage and difficulty of the questions and evaluate their ability to recommend part of the text that a student should re-read based on the student's responses to the questions. Based on our evaluation of ChatGPT and Bard, we report that, 1) Large language models are able to generate high quality meaningful questions that have high correlation with the input text, 2) They generate diverse question that cover most topics in the input text even though this ability is significantly degraded as the input text increases, 3)The large language models are able to generate both low and high cognitive questions even though they are significantly biased toward low cognitive question, 4) They are able to effectively summarize responses and extract a portion of text that should be re-read.

Existing question answering (QA) datasets are created mainly for the application of having AI to be able to answer questions asked by humans. But in educational applications, teachers and parents sometimes may not know what questions they should ask a child that can maximize their language learning results. With a newly released book QA dataset (FairytaleQA), which educational experts labeled on 46 fairytale storybooks for early childhood readers, we developed an automated QA generation model architecture for this novel application. Our model (1) extracts candidate answers from a given storybook passage through carefully designed heuristics based on a pedagogical framework; (2) generates appropriate questions corresponding to each extracted answer using a language model; and, (3) uses another QA model to rank top QA-pairs. Automatic and human evaluations show that our model outperforms baselines. We also demonstrate that our method can help with the scarcity issue of the children's book QA dataset via data augmentation on 200 unlabeled storybooks.