# Revisiting The Classics: A Study on Identifying and Rectifying Gender Stereotypes in Rhymes and Poems

arXiv: https://arxiv.org/abs/2403.11752 · HTML: https://mlsanity.com/paper/2403.11752/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 18 Mar 2024.

## Builds on (most cited first, 9 of 9)

- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [Reducing Gender Bias in Abusive Language Detection](https://mlsanity.com/paper/1808.07231/) (2018)
- [Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling](https://mlsanity.com/paper/1906.07241/) (2019)
- [Do Neural Ranking Models Intensify Gender Bias?](https://mlsanity.com/paper/2005.00372/) (2020)
- [Shirtless and Dangerous: Quantifying Linguistic Signals of Gender Bias in an Online Fiction Writing Community](https://mlsanity.com/paper/1603.08832/) (2016)
- [Debiasing Embeddings for Reduced Gender Bias in Text Classification](https://mlsanity.com/paper/1908.02810/) (2019)
- [ChatGPT Participates in a Computer Science Exam](https://mlsanity.com/paper/2303.09461/) (2023)
- [Quantifying and Reducing Stereotypes in Word Embeddings](https://mlsanity.com/paper/1606.06121/) (2016)
- [How does Grammatical Gender Affect Noun Representations in Gender-Marking Languages?](https://mlsanity.com/paper/1910.14161/) (2019)

## Abstract (arXiv)

Rhymes and poems are a powerful medium for transmitting cultural norms and societal roles. However, the pervasive existence of gender stereotypes in these works perpetuates biased perceptions and limits the scope of individuals' identities. Past works have shown that stereotyping and prejudice emerge in early childhood, and developmental research on causal mechanisms is critical for understanding and controlling stereotyping and prejudice. This work contributes by gathering a dataset of rhymes and poems to identify gender stereotypes and propose a model with 97% accuracy to identify gender bias. Gender stereotypes were rectified using a Large Language Model (LLM) and its effectiveness was evaluated in a comparative survey against human educator rectifications. To summarize, this work highlights the pervasive nature of gender stereotypes in literary works and reveals the potential of LLMs to rectify gender stereotypes. This study raises awareness and promotes inclusivity within artistic expressions, making a significant contribution to the discourse on gender equality.