Revisiting The Classics: A Study on Identifying and Rectifying Gender Stereotypes in Rhymes and Poems
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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.
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References · 9
- 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 911
- 2018Reducing Gender Bias in Abusive Language Detectioncited by 152
- 2019Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modelingcited by 71
- 2020Do Neural Ranking Models Intensify Gender Bias?cited by 28
- 2016Shirtless and Dangerous: Quantifying Linguistic Signals of Gender Bias in an Online Fiction Writing Communitycited by 21
- 2019Debiasing Embeddings for Reduced Gender Bias in Text Classificationcited by 19
- 2023ChatGPT Participates in a Computer Science Examcited by 19
- 2016Quantifying and Reducing Stereotypes in Word Embeddingscited by 14
- 2019How does Grammatical Gender Affect Noun Representations in Gender-Marking Languages?cited by 14
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